Facial liveness detection in image biometrics

ABSTRACT

System and techniques for spoofing detection in image biometrics are described herein. A sequence of images may be obtained from a camera; a first plurality of images in the sequence of images including a representation of a user body part, and a second plurality of images in the sequence of images including a representation of an environment of the user. A marker may be created for the representation of the body part. A feature of the environment of the user present during the second plurality of images may be identified in the sequence of images using a third group of circuits. A correlation between the marker and the feature of the environment in the sequence of images may be quantified to produce a synchronicity metric of the degree to which the marker and the feature of the environment correlate.

CLAIM OF PRIORITY

This patent application is a U.S. National Stage Application under 35U.S.C. 371 from International Application Number PCT/US2015/022934,filed Mar. 27, 2015, which claims the benefit of priority to all of:U.S. Provisional Application Ser. No. 62/079,011, titled “LIVENESSDETECTION IN FACIAL RECOGNITION WITH SPOOF-RESISTANT PROGRESSIVE EYELIDTRACKING,” and filed Nov. 13, 2014; U.S. Provisional Application Ser.No. 62/079,020, titled “FACIAL SPOOFING DETECTION IN IMAGE BASEDBIOMETRICS,” and filed Nov. 13, 2014; U.S. Provisional Application Ser.No. 62/079,036, titled “COVERT LIVENESS DETECTION SYSTEM AND METHOD,”and filed Nov. 13, 2014; U.S. Provisional Application Ser. No.62/079,044, titled “FACIAL SPOOFING DETECTION FACILITATION,” and filedNov. 13, 2014; U.S. Provisional Application Ser. No. 62/079,082, titled“SCREEN REFLECTION ANTI-SPOOFING SYSTEM AND METHOD,” and filed Nov. 13,2014; U.S. Provisional Application Ser. No. 62/079,095, titled “EYELIDTRACKING FOR BIOMETRIC LIVE-NESS TEST,” and filed Nov. 13, 2014; andU.S. Provisional Application Ser. No. 62/079,102, titled “SPOOFINGDETECTION IN IMAGE BASED BIOMETRICS,” and filed Nov. 13, 2014; theentirety of all are hereby incorporated by reference herein.

TECHNICAL FIELD

Embodiments described herein generally relate to biometric computerauthentication and more specifically to facial liveness detection inimage biometrics.

BACKGROUND

Facial recognition for authentication purposes allows a user to use herface to authenticate to a computer system. Generally, the user's face iscaptured and analyzed to produce and store a feature set to uniquelyidentify the user during a set-up process. When the user wishes to useher face in a future authentication attempt, a camera will capture arepresentation of the user's face and analyze it to determine whether itsufficiently matches the stored feature set. When a sufficient matchbetween a current image capture of the user's face and the storedfeature set is made, the user is authenticated to the computer system.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various embodiments discussed in the presentdocument.

FIG. 1 is a block diagram of an example of an environment including asystem for image biometrics, according to an embodiment.

FIG. 2 is an example of a scene visible by a camera for imagebiometrics, according to an embodiment.

FIG. 3 is a diagram of an example arrangement of system and scenecomponents for image biometrics, according to an embodiment.

FIG. 4 is a diagram of an example of scene reflectivity to non-visiblelight, according to an embodiment.

FIG. 5A is an example of an emitted pattern and FIG. 5B is an example ofscene reflectivity under the emitted pattern, according to anembodiment.

FIG. 6 is an example of distance planes in a scene, according to anembodiment.

FIG. 7 is an example of eye gaze correlation to a proffered prompt,according to an embodiment.

FIG. 8 is a block diagram of an example of user interface for imagebiometrics, according to an embodiment.

FIG. 9 is a scene line transformation with identified face markers,according to an embodiment.

FIG. 10 is a scene simplification of a scene line transformation withidentified face markers, according to an embodiment.

FIG. 11 is a sequence of an example user interface in which a target ispresented to the user, according to an embodiment.

FIGS. 12A and 12B are example user interfaces of a target promptpresented to a user in which the scene is obfuscated, according to anembodiment.

FIG. 13 is an example gaze vector determination to facilitate targetplacement, according to an embodiment.

FIG. 14 is an example of a sequence of eyelid movements in a blink,according to an embodiment.

FIG. 15 is a sequence of frames tracking blink characteristics of aneye, according to an embodiment.

FIG. 16 is an example activity analysis of a blink for each of two eyes,according to an embodiment.

FIG. 17 is an example activity analysis of a blink for each of two eyes,according to an embodiment.

FIG. 18 illustrates a flow diagram of an example of a method forspoofing detection in image biometrics, according to an embodiment.

FIG. 19 illustrates a flow diagram of an example of a method for facialliveness detection in image biometrics, according to an embodiment.

FIG. 20 is a block diagram illustrating an example of a machine uponwhich one or more embodiments may be implemented.

DETAILED DESCRIPTION

Some current facial recognition solutions are prone to be spoofed (e.g.,faked) by a photo, for example, on a phone, tablet, etc. For example, aperson may acquire a picture of a user, for example, via a tablet cameraor from an employee's social media profile, display that picture on adevice and hold the device in front of a camera being used to capture afacial representation to authenticate the user. Thus, the person hasspoofed the user's real face to circumvent the face-based authenticationprocess.

To prevent facial spoofing, some types of anti-spoofing techniques maybe employed (alone or in combination), such as asking users to performcertain tasks (e.g., invoking dynamic user involvement that may be hardto predict to complicate spoofing) or analyzing the context (e.g.,environment) of the image capture to determine whether the capturedimage represents a “live” user. Both techniques may distinguish betweena live user and a facsimile, but merely asking for more user input(e.g., in the form of specific tasks, assuming body positions, etc.) maypresent some user experience challenges (e.g., how to represent therequest, difficulty for user's to follow the request, excessive time inperforming the requests, etc.). Analyzing the context in which the imagewas captured, however, may avoid the user experience challengesassociated with a more interactive technique. Further, techniques may beemployed to induce a user behavior without a specific instruction. Forexample, simply presenting an interesting object that is moving willcapture a user's attention without the user having to be told to trackthe object. Such techniques may augment environmental observationwithout burdening the user.

Mechanisms and techniques for analyzing authentication context aredescribed below. As used herein, the context does not include suchcontexts as the time of day, location, or other discernable contexts,but rather the environmental contexts observable from a camera or othersensor on the authentication device or introduced to the user (e.g., amoving target on the screen). Also, although a technique may bedescribed below to identify a spoofing attempt (e.g., the use of afacsimile instead of a real biometric component to authenticate a user),the same techniques may be used to identify a live biometric component.Thus, even in environments in which spoofing is not a concern—such as ina family home whether authentication may be primarily used to identifyusers for application or environment customization rather than accesssecurity—identifying a live face may reduce processing overhead ornegative authentication attempts by avoiding authentication processingon inanimate representations of body parts (e.g., a doll held in achild's arms).

FIG. 1 is a block diagram of an example of an environment 100 includinga system 105 for image biometrics, according to an embodiment. Thesystem 105 may include a sensor 120 (e.g., a digital camera or videorecorder, a non-visible light detector, etc.), a display 145 (e.g., ascreen, monitor, visible light emitter, etc.), optionally an additionalemitter 150 (e.g., an infrared (IR) or other non-visible or visiblelight spectrum emitter), an environmental feature detector 125, abiometric feature detector 130, a synchronicity detector 135, and aspoofing indication controller 140. The spoofing indication controller140 may also be known as a liveness indication controller when theanalysis is shifted from identifying a spoofing attempt to identifying alive body part (e.g., face). The system 105 may also include anauthentication controller (not shown) to actually perform anauthentication of a user 115. Each of these components is implemented incomputer hardware, such as a circuit set, as described below withrespect to FIG. 20.

The system 105 may obtain a sequence of images from the sensor 120. Thesequence of images includes a first plurality of images (e.g., frames,pictures, etc.) including a representation of a user body part (e.g.,face, finger, etc.). As used herein, the representation of the body partis the sensor representation of the actual part. Thus, a digital imageof the user's face is the representation of the face. The sequence ofimages also includes a second plurality of images that include arepresentation of an environment of the user 115. This first pluralityand second plurality of images may fully or partially overlap (e.g.,include the same images or other sensor readings). As illustrated, thesensor 120 is a camera with a field of view 110 that encompasses theuser 115 as well as some other objects, such as the background of theuser 115. In an example, the sequence of images may be processed toreduce noise (e.g., application of a filter). In an example, thesequence of images may be processed to reduce color information. In anexample, the color information may be reduced to one bit per pixel(e.g., black and white).

In an example, the sequence of images may be captured as the user 115approaches the sensor 120. For example, the relevant images in acontinuous stream may be bounded by the identification of the user 115approaching the sensor 120, or activating the sensor 120 upon anadditional sensor (not shown) indicating that the user 115 isapproaching (e.g., via device positioning or location based services, amotion detector, etc.). Such early activation (e.g., before anauthentication request is made) may provide additional contextinformation (e.g., relative motion between the user 115 and otherenvironmental components) that may be used in the techniques describedbelow.

The biometric feature detector (BFD) 130 may obtain the sensor 120 dataand create a marker for the representation of the body part. As usedherein, the marker is a transformation or alternative representation ofthe body part and is specific to the liveness, or spoofing test beingapplied. For example, a location of the eyes in a representation of aface may be the only positioning information needed to ascertain alocation of the face in the image. Accordingly, a marker may be a pointrepresenting the center of the eyes.

In an example, the marker may be a measurement of the body part based ona depth measurement (described below). Such a measurement may beperformed by scaling the pixel representation of the body part by thedepth associated with the area in which the body part was detected. Sucha measurement may not directly translate to the real-life size of thebody part, but more distant objects will be scaled greater than nearobjects. In an example, an optical characteristic of the sensor 120 maybe used to scale the body part using the depth measurement. Althoughsuch a mechanism may be more complicated that the simple scalingmechanism it is likely to result in a more accurate determination of thesize of the corresponding body part. In an example, the measurement ofthe body part based on the depth measurement and the representation ofthe body part may be determined by extrapolating the size of the bodypart using the depth image and a dimension (e.g., length, width, height,volume, etc.) of representation of the body part. That is, a singledimension may be scaled rather than scaling the entirety of the bodypart representation. In an example, the measurement may include a depthmap of the body part. Such a map may provide contour details that may beused to correlation purposes to a model of the body part maintained bythe system 105.

In an example, the marker may be a line between the eyes in therepresentation of the body part. In an example, the marker may be apoint in the middle of the line. In an example, the marker may be ashape centered at the centroid of the representation of the body part.

In an example, the BFD 130 may track a gaze of an eye from therepresentation of the body part. Thus, the BFD 130 may determine adirection that the eyes are looking at any given moment. In an example,this tracking occurs across a plurality of the images in the sequence ofimages. In an example, the gaze of the eye may be determined bymeasuring the amount of iris visible for the eye. In an example, acalibration session may be used to calibrate the eye tracking by the BFD130. In an example, the calibration session may include presenting amoving prompt. A second (e.g., different) sequence of images may becaptured during the presentation of the moving prompt. A correspondenceof the user's 115 eyelid positions to the moving prompt may be measuredfrom the second sequence of images. The measured correspondence may thenbe used as the spoofing indication attempt threshold described below.

In an example, the marker may be a measured eye blinking sequence. Thatis, the blink of one or more eyes may be tracked, with characteristicsof the eye measured throughout the blink.

The environmental feature detector (EFD) 125 may obtain the sensor 120data and identify a feature of the environment of the user 115 that ispresent during the second plurality of images in the sequence of images.In an example, identifying the environmental feature may includeemitting a pattern into the environment, for example, using the display145 or the emitter 150. A uniform pattern is the same (within tolerance)across space and time. Such a uniform pattern may include a spotlight,or other illuminator. A non-uniform pattern varies either in space ortime, the wavelength or amplitude of the emission. This variance is bydesign, rather than by accident. For example, the wave pattern depictedin FIG. 5 is a non-uniform pattern whereas a globe-shaped emitter, suchas a light bulb, is a uniform pattern emission even though there may bevariances in the emission.

Pattern emission may serve several useful details about the environmentwithout burdening the user 115 with tasks. For example, the emission maybe used to determine distances to environmental components. Further,some surfaces of those environmental components may have differentreflection characteristics that may be used to distinguish between adevice screen, for example, and a live face. In an example, the patternmay be uniform. In an example, the pattern may be non-uniform. In anexample, the pattern may be in the visible light spectrum. In anexample, the pattern may be in a non-visible light spectrum. In anexample, the non-visible light spectrum of the pattern includes IRlight.

In an example, the pattern may be emitted in a non-uniform pattern in avisible light spectrum. In an example, the non-uniform pattern is amoving pattern. A moving pattern involves the variation in wavelength oramplitude over an area in time such that it appears to the user 115 thata portion of the pattern moves from one place to another. For example, ablack horizontal bar traversing from the top to the bottom of thedisplay 145. In an example, the pattern is emitted in a non-visiblespectrum, for example by the emitter 150. In an example, the non-visiblespectrum emission is an IR emission.

In an example, the feature of the environment is a depth measurement ofthe body part derived from the pattern. For example, the time-of-flightof the emission reflecting off of a surface may be used to measuredepth, or distance, from the emitter to the surface. Other mechanisms,such as emitting a known pattern and ascertaining variances in thepattern's reflection, may also be used. For example, a pattern of dotsmay be emitted. The distance between the reflected dots will be greaterfor closer objects and lesser for dots reflecting off of distanceobjects. Similar analysis may be performed in either the visible ornon-visible light spectrums. In the visible spectrum, the pattern may bea background to the authentication screen. In an example, the visiblelight spectrum pattern may include high contrast elements, such as blackon white or white on black.

In an example, the feature of the environment may be a reflectivity ofthe pattern in an area of the environment containing the marker. Devicesand inks often absorb IR light but reflect, in a relatively uniformmanner, visible light. Conversely, for example, the human face reflectsIR and is a poor reflector of visible light. Moreover, the flat surfaceof a device or of paper and the contoured nature of most body parts usedfor authentication result in different reflection characteristics evenif both reflect the emission.

In an example, the feature of the environment may include a convex hullof points on edges detected in the plurality of images. That is, inanalyzing detectable edges in the sequence of images, the edges maysuggest an enclosing shape. The convex hull is constructed when theedges intersect, or conform to expected intersections (e.g., do notactually intersect but come close enough given a shape model) and theintersected edges enclose an area. In an example, the edges may becompared to a catalog of device edge configurations to determine whetherthe edges represent a known device. In this example, the convex hull ofpoints is based on a physical configuration of the known device. In anexample, the edges are moving edges. That is, non-moving edges areexcluded from the convex hull construction. This example, recognizes thelikelihood that a spoofing device is mobile within the field of view 110as opposed to, for example, a piece of art hanging in an office.

In an example, the feature of the environment may be a backgroundproximate to the body part. As used herein, proximate denotes an areathat borders the body part, includes separately identifiable features,and is not part of the body part. Proximity may be based on a thresholddistance. Thus, elements bordering the body part in the sequence ofimages out to a threshold distance are considered proximate to the bodypart in the real-world scene.

In an example, proximity may include objects that intersect therepresentation of the body part. Thus, a detected credenza that touchesthe representation of the body part may be considered proximate. Thus, aspecific threshold distance need not be applied as long as the objecttouches the body part in the sequence of images. It is noted that the“touching” need not be actual touching in the real-world compositionbecause an object passing behind the body part will appear to touch therepresentation of the body part when flattened to a two-dimensionalrepresentation in the sequence of images.

The proximity between the background and the representation of the bodypart simply addresses the issue of the authentication environmentbackground also being in the field of view with the spoofing device.Thus, although a background will surround the body part as displayed bythe device, the authentication environment background may also bedetectable. However, the feature of importance here is the backgroundcharacteristics around the body part that would be difficult to varywith respect to the body part as the device is moved.

In an example, the environmental feature is model of an eye blinkingsequence. In this example, the environmental feature is a function ofthe environment but not directly observable from the environment itself.The eye blinking model provides a basis to which a measured eye blinkingsequence may be compared. The eye blinking model provides expectedcharacteristics of a live blink by the user 115. Different eye blinkingmodels may be provided based on environmental conditions, such aslighting conditions likely to increase a blink rate, or othercharacteristic, of the user's 115 blink. In an example, the model maydefine an eyelid sequence corresponding to eyelids that move in oppositedirections as an abnormal eyelid sequence.

The synchronicity detector (SD) 135 may quantify a correlation betweenthe marker and the feature of the environment in the sequence of imagesto produce a synchronicity metric of the degree to which the marker andthe feature of the environment correlate. Thus, the marker and theenvironmental feature are compared to determine how close they are. Avalue is then assigned to this closeness. The value may be one ofseveral discrete values, a real representation (e.g., a numericalrepresentation to the precision allowed by the computing hardware), abinary representation, etc. The specific quantification (e.g., measureof closeness) will depend on the specific environmental feature andmarker used.

In an example, where the environmental feature is a depth measurement tothe body part and the marker is a measurement of the body part, thecorrelation may be the degree to which the measurement of the body partconforms to a live body part at the depth measurement. Thus, a range ofacceptable body part sizes may be maintained. If the body partmeasurement falls outside of the maintained range, the degree to whichthis occurs may be synchronicity metric. In an example, thesynchronicity metric may be a binary indication as to whether or not themeasurement falls within the range.

In an example, the correlation between the feature of the environmentand the marker may be the degree to which the area reflects the pattern.For example, in an IR emission, a device will not be expected to reflectmuch IR light. Thus, there should be a higher correlation between thearea in which the body part was discovered and the reflectioncharacteristic if the body part is displayed by the device. Conversely,if the body part is real, then a lower correlation may be assigned. Thedirection of the correlation, e.g., high or low, may be reversed if adevice is expected. In an example, the pattern is moving, and the degreeto which the area reflects the pattern includes determining whether aspecified feature of the patter moved an expected distance. For example,if a straight light moving pattern is projected on a flat surface, theprogress of the line in the reflection should be uniform. However, ifthe pattern were moving across a contoured surface, such as a face orfingertip, the pattern would be distorted and thus would not move theexpected distance. In an example, the degree to which the area reflectsthe pattern may include the homogeneity of reflection of the light.

In an example, where the environmental feature includes the convex hulldescribed above, the correlation between the marker and the feature ofthe environment may be the degree to which marker is found within theconvex hull. Thus, a greater correlation may be ascribed to the markerbeing within the convex hull, and a lesser correlation may be ascribedto the marker being outside the convex. Additional gradients in thecorrelation may be ascribed given relative distances between the markerand an edge of the convex hull. Thus, a lesser correlation may beascribed to a marker father outside the convex hull than a second markerthat is still outside the convex hull yet closer to the hull.

In an example, the SD 135 may measure the degree to which a tracked gazeof an eye tracks a moving pattern displayed on the display 145. Thus, aninstinctual tendency for a person to visually track a moving prompt maybe exploited to determine liveness without asking the user 115 toperform a specific task. In this example, a live person would beexpected to track the moving pattern, or a portion thereof, while afacsimile would have difficulty simulating such tracking.

In an example, where the environmental feature is a background proximateto the body part, the correlation may be the degree to which thebackground proximate to the body part in the sequence of images moveswith the marker. Thus, if the proximate background moves with therepresentation of the body part, it may be assumed that the backgroundis part of a picture including the body part and thus a spoofingattempt. Conversely, if the proximate background remains still while therepresentation of the body part moves, it may be assumed that thecorresponding body part belongs to a live being. This correlationmeasurement involves movement of the body part across the background.Although this often happens naturally, it may also be facilitated. Forexample, a target may be displayed on the display 145 to the user 115.Such a target is not a specific instruction to the user 115, but ratherrelies on the user's 115 natural inclination to move towards the target.

In an example, the target may be displayed along with a representationof the body part to the user 115. In this example, the target isdisplayed in a different position than the representation of the bodypart. Thus, the user 115 will be inclined to move so as to place thebody part on the target. It is not important that the user actuallyreach the target, but rather that some movement occurs. In an example,the target is an outline of a shape sized such that the representationof the body part fits inside the shape when a center of the shapecoincides with a center of the representation of the body part. Forexample, the outline of a circle may be the target, the circle beinglarge enough to fit a representation of the user's 115 face inside ofit. Thus, the user 115 may be inclined to try and place her face insidethe circle. In an example, the shape may be a rectangle, an ellipse, astar, or a triangle. In an example, the target is a shape sized suchthat the representation of the body part does not fit inside the shapewhen a center of the shape coincides with a center of the representationof the body part. In an example, the target may be an icon, shape,point, or other differentiating visual element.

In an example, the target being in a different position than therepresentation of the body part may include displaying the target at anoffset from the body part. In an example, the offset may be determinedby a relative motion model defining a motion parameter of the body partto distinguish between foreground objects and background objects in afield of view 110 of the sensor 120 capturing the sequence of images.The relative motion model thus indicates the amount of motion that willallow a differentiation between foreground objects, like the body part,and background objects. In an example, the relative motion model mayinclude a threshold of movement sufficient to distinguish betweenforeground objects and background objects.

In an example, the representation of the body part may be a symbolobfuscating at least one feature of the body part from the sequence ofimages. In an example, the symbol may be an illustration of the bodypart. Obfuscating the representation of the body part or other portionsof the displayed target scenario provides benefits to the manipulationsthat may be performed in showing motion of the representation of thebody part to the target described below. Further, such obfuscations maymake it difficult for a malicious person to precisely ascertain what isbeing monitored by the system 105 and thus harder to create a realisticfacsimile for spoofing purposes.

In an example, the body part may be a face, and the direction of theoffset is the inverse of the orientation of the face. Thus, the targetis placed in a direction forward of the user's 115 forward motion. Thismay result in a more intuitive motion from the user's 115 perspective.In an example, the distance of the offset from the representation of thebody part may not be coupled to a pixel position difference between thebody part captured in the sequence of images and the target. In anexample, displaying the representation of the body part may includemodifying the motion of the body part using the relative motion model.This modification may include speeding up or slowing down the movementat various points throughout the moving. Thus the target may be shifted,grown, shrunk, etc., or the representation of the body part may betranslated within the displayed image. This manipulation may elicit agreater motion from the user while indicating progress in reaching thetarget to the user 115. This technique may be easier when the scene isobfuscated to the user 115 as described above.

In an example, where an eye blinking sequence is measured, thecorrelation may be the degree to which the measured eye blinkingsequence conforms to the eye blinking model. In this example, a strongcorrelation indicates a live person whereas a poor correlation indicatesa spoofing attempt. In an example, the degree to which the measured eyeblinking sequence conforms to the model may be determined by processing,through a pattern recognizer, the series of respective scores calculatedfrom a percentage of the eye unobstructed by the eyelid. In thisexample, the pattern recognizer checks the series of respective scoresfor at least one of an abnormal eyelid sequence or an abnormal blinksequence based on the model. In an example, the pattern recognizer mayserially check the abnormal blink sequence after verifying that theeyelid sequence is normal. That is, the eyelid sequence check isperformed first. If it passes, then the abnormal blink sequence ischecked.

The spoofing indication controller (SIC) 140 may provide a spoofingattempt indication in response to the synchronicity metric being beyonda threshold. In an example, the SIC 140 may provide a livenessindication or otherwise classifies a representation of a body part aslive in addition to, or instead of, providing the spoofing indication.In an example, the authentication attempt may be denied with thespoofing attempt indication. In an example, the spoofing attemptindication may be combined with other spoofing attempt indications todetermine whether to deny the authentication attempt. For example, eachspoofing attempt indication may be a weighted value. Adding the weightedvalues results in a total that is compared to a threshold to determinewhether an active spoofing attempt is being made; authentication beingdenied during an active spoofing attempt. In an example, the spoofingindication is provided to another system component to be used in theauthentication process.

In an example, where the reflectivity of the area in the environment isthe basis for the synchronicity metric, the threshold may define aminimum reflectivity of the pattern. That is, if the device is expectedto reflect the emission, then a spoofing attempt indication is made whenthe area reflects enough (e.g., more than the threshold). In an example,the minimum reflectivity includes a minimum reflectivity of a specifiedfeature of the pattern. Such features may include a visual feature(e.g., a portion of emitted lines or shapes), a wavelength, etc.

In an example, where the reflectivity of the area in the environment isthe basis for the synchronicity metric, the threshold may define amaximum reflectivity of the pattern. That is, if the device is notexpected to reflect the emission (in the case of IR light), then aspoofing attempt indication is made when the area does not reflectenough light (e.g., less than the threshold). In an example, where thepattern is in a non-visible light spectrum, the degree to which the areareflects the pattern includes the homogeneity of reflection of thenon-visible light. In an example, the degree to which the area reflectsthe pattern may include a brightness of the reflection of non-visiblelight, a bright reflection corresponding to a high reflectivity. In thisexample, a highly reflective surface in the non-visible spectrum willnot be considered a spoofing device. Rather, a device would be expectedto have a low reflectivity that is homogenous.

In an example, the SIC 140 may provide a spoofing indication attempt inresponse to the degree to which a tracked eye gaze tracks a movingpattern provided in the display 145. As noted above, if the degree islow, indicating poor or non-existent tracking, it is likely that thecorresponding body part is not real. Conversely, a high degree oftracking indicates that a live person is being represented in therepresentation of the body part.

In an example, where the correlation is the degree to which theproximate background moves with the representation of the body part, theSIC 140 may provide the spoofing indication when the degree is low. Inan example, the spoofing attempt indication may be made before therepresentation of the body part reaches the target. That is, thespoofing attempt indication does not rely on the user actually movinginto the target, but rather after a requisite amount of motion hasoccurred to correlate the relative motion of the representation of thebody part to the proximate background.

FIGS. 2-17 illustrate various features of the embodiments describedabove with respect to FIG. 1. Generally, one or more of the figuresillustrate the interaction between the EFD 125, BFD 130, and SD 135 withrespect to specific embodiments.

FIG. 2 is an example of a scene 210 visible by a camera for imagebiometrics, according to an embodiment. FIG. 2 illustrates environmentalcomponents, such as a representation 205 of the user 115, a device 220,a face 215 on the device 220, and other background components. The scene210 is the pictorial representation of the components within the fieldof view 110 illustrated on FIG. 1. This scene 210 includes the elementsused in most embodiment examples illustrated in FIGS. 3-13.

FIG. 3 is a diagram of an example arrangement 300 of system and scenecomponents for image biometrics, according to an embodiment. As notedabove, emitting light into the environment may produce usefulmeasurements to determine environmental features. The arrangement 300includes the sensor 120 and the display 145 for emission purposes. In anexample, the emitter 150 may be used in addition to the display 145, oralone, to emit light into the environment. In the arrangement 300, theuser 115 is holding the device 220. The solid arrows indicate thereflection of light from the device 145 to the sensor 120 after beingemitted by the display 145. Similarly, the dashed arrows indicate thereflection of light from the user 115 to the sensor 120 after beingemitted.

FIG. 4 is a diagram of an example of scene reflectivity 405 tonon-visible light, according to an embodiment. As illustrated, thedarker the shading, the greater the level of non-visible lightreflectivity. Thus, the device 220 is homogenously reflecting light at alevel similar to the background 410 and the representation 205 of theuser 115 indicates a non-homogenous reflection of the light greater thanthe background 410 levels. As noted above, the display of device 220 andmany commercial inks generally reflect IR light poorly whereas humanskin does not. Combining the scene reflectivity 405 with the scene 205allows the reflectivity in a region to facilitate determination as towhether the representation 205 is live, by finding non-homogenousreflectivity at the same location in the sequence of images, or afacsimile face 215 by noting a homogenous reflection, or lack ofreflection, at the location of the device 220.

FIG. 5A is an example of an emitted pattern and FIG. 5B is an example ofscene reflectivity 505 under the emitted pattern, according to anembodiment. In this example, the illustrated wave pattern is non-uniformand emitted by the display 145. Such an emission will project into theenvironment and reflect off of surfaces therein to be measured by thesensor 120, as illustrated in FIG. 5B. Conversely to the non-visiblereflectivity described above, visible light reflectivity will beexpected to be greater on the shiny surface of the device 220. Thus, thepattern is observable on the device 220 but not on the representation205 of the user 115. In an example, where the pattern is sufficientlyreflected from the user 115, the non-uniform nature of the user's 115face will result in distortions to the pattern that may be observed. Ineither case, the device 220 has a differentiating reflectioncharacteristic from that of the user 115.

The following describes additional features for this technique. Thistechnique capitalizes on the reflective properties of many of thespoofing mediums by projecting a known pattern into the environment thendetecting if a reflection is present on the spoofing image. Detection ofthis unique pattern in the sensor's 120 image suggests a spoofing device220 is being used in place of the user's 115 face. Such an approach isbetter than, for instance, an anti-spoofing technique based on directed,or scripted, user movement because it avoids inconveniencing the userwith socially observable directed movements (e.g. scripted blink or posemovements), making it transparent during the log-in identificationstage. Doing so also maintains the initial ease-of-access promise offeature-based authentication.

In an example, a seemingly innocuous welcome screen is placed on thedisplay 145. When the user 115 attempts to log in, no reflection isdetected off their face due to the diffusion of the pattern on the faceand clothes. However, when the device 220, such as a phone or tabletpresents an individual's face 215 to the sensor 120, the pattern of thewelcome screen as projected by the display 145 is reflected from thescreen of device 220. In an example, the features of the pattern aretracked across multiple images in the sequence of images. If the signalto noise ratio is above a threshold, then this face 215 is declaredinvalid.

In an example, the pattern includes features that separate well fromorganic shapes found on the human face, upper torso, or other items ofthe body or worn on the body. In an example, pattern is supplied as partof the BFD 130. In an example, the pattern is part of the hardware,software, or firmware of the sensor 120. In an example, the pattern issupplied by the user 115. In an example, when pattern is submitted bythe user 115, analysis and feature extraction may be performed on theimage by system 105 to ensure the pattern may be detected when off ofthe device 220.

In an example, the pattern may move across the display 145. The dominantcharacteristic of a static image is the sharp edge contrast of straightlines. In an example, a moving set of high contrast line edges may sweepacross the display 145 at a fixed rate of movement and thus be effectiveat detecting static images. This approach also has the benefit ofincreasing the signal to noise ratio. It also avoids the problem offeatures of the static pattern falling on parts of the face (e.g., abeard) that obscure the important aspects of the pattern. In an example,the system 105 may search for the features of the swept line or lines inthe sensor's 120 field of view 110. If a pattern feature is detected inan image, the location of that feature in subsequent images is predictedusing knowledge of the sweep rate and the sensor 120 sample time. Inthis way, additional certainty is achieved that the reflected pattern isbeing generated by the system 105.

FIG. 6 is an example of distance planes 605 and 610 in a scene 600,according to an embodiment. As noted above, the distance planes 605 and610 may be determined via pattern scaling in the visible light spectrum,or pattern scaling, deformation, or time-of-flight in the non-visiblelight spectrum via emissions from the display 145, the emitter 150, orboth. In a typical scenario, the device 220 would need to be held closeto the sensor 120 in order to have the face 215 be large enough toattempt an authentication. When this proximity is compared to that ofthe representation 205 of the user 115, it becomes apparent that theface 215 is too small to be a valid human face.

FIG. 7 is an example of eye gaze correlation to a proffered prompt,according to an embodiment. FIG. 7 illustrates a sequence 700 of frames705-730, each frame including a line position and corresponding eyeposition. As noted above with respect to FIGS. 1 and 5, a visible movingpattern may be emitted from the display 145. As the sequence 700progresses, the pattern sweeps from the top to the bottom and back upagain. The eye, a real eye, tracks the moving pattern as humans areinstinctually inclined to do. In an example, the moving element may movein a non-linear fashion, such as stopping half-way down the display 145and moving back up or jumping from the top of the display 145 to thebottom. In an example, the eye tracking may be performed by eyelidpositioning (e.g., tracking eyelid position and inferring eye gazedirection).

In an example, the moving pattern may take many graphic forms includingshapes (e.g., a horizontal line, star, etc.), pictures, oralphanumerical characters. Each time an authentication request isreceived, the moving pattern may start at a random spot on the display145. In an example, the moving pattern does not span the entire display145. For example, the moving pattern may be displayed within a window ofa web browser. In an example, the moving pattern may be displayed on aseparate device from where the authentication request originated. Forexample, a user 115 may carry a companion device that includes a cameraand display screen for presentation of the moving pattern.

In an example, the sequence of images may be synchronized with themoving pattern. Synchronization may be time-based orpattern-location-based. For example, in a time-based approach, thesequence of images may be stored with a time-stamp of ‘0’ at the momentthe moving pattern is first displayed. Time-stamps may also be storedfor each location of the moving pattern during the authenticationsequence (e.g., the display of the moving pattern). When the video datais stored using a pattern-location-based approach the sequence of imagesincludes an indication of where (e.g., the absolute coordinates orrelative location to one or more edges of the display 145) the movingpattern was on the display 145 when the sequence of images werecaptured.

In an example, the sequence of images is analyzed to determine whetherthe position of a user's 115 eyelid is consistent with the position ofthe moving pattern. For example, an eyelid position of fully open may beconsidered to be consistent with the moving pattern near the top of thedisplay 145. In an example, an eye tracking technique is used to locatea user's 115 eye(s) and determine the amount of the iris that isvisible. The amount the iris visible may be used as a proxy for theposition of the eyelid. Thus, if the iris is completely visible theposition of the eyelid may be 100% open or fully open. In variousexamples, the amount of the iris may be measured from the top or bottom.In other words, the iris may be covered from the bottom or top.

FIGS. 8-10 illustrate the use of a convex hull for liveness detection.This technique exploits the likelihood that a face framed in edges,especially moving edges, is unlikely to be a live face.

FIG. 8 is a block diagram of an example of user interface 805 for imagebiometrics, according to an embodiment. The interface 805 may includethe sequence of images in a viewing port 810. As shown, the viewing port810 includes a face 215 as well as the device 220 with a representation205 of the user's 115 face.

FIG. 9 is a scene line transformation with identified face markers,according to an embodiment. The image illustrated in FIG. 8 may beanalyzed to discern relative motion of environmental features andmarkers. As illustrated, dashed lines are non-moving edges, dotted linesare curved lines (e.g., not straight edges) in the image, and solidlines are moving edges. The BFD 130 may detect the represented faces andcreate the marker 905 for the live face and the maker 910 for thespoofed face. Further, the EFD 125 may determine that the edges 915 aremoving together and enclose an area. The SD 135 and SIC 140 maydetermine that the marker 910 is within the moving edges 915, and thussuch a face is a possible spoofing attempt that should be flagged orotherwise fail to authenticate the user 115. In contrast, the marker 905is not within the edges 915 and so may not be so easily assessed as aspoofing attempt. However, if the marker 905 moves synchronously withthe moving edges 915, it may be determined that everything in the imageis a facsimile of the user's environment—although in the illustratedexample, this is unlikely because some of the edges are not movingrelative to the moving edges 915 as evidenced by the dashed lines.

FIG. 10 is a scene simplification of a scene line transformation withidentified face markers, according to an embodiment. The moving edges ofFIG. 9 may include more complexity than is necessary for performing theanalysis (e.g., the display edges within the device edges as illustratedin FIG. 9). Further, it may be unnecessary to determine the orientationof a facial representation as long as the marker is determinable. Thesimplification of a scene line transformation provides for thesesimplifications by creating a convex hull 1015 based on a set of cornersfrom the moving edges. As shown, the convex hull 1015 encompasses anarea including the device's 220 edges. Further, markers 910 and 905 maybe changed to secondary markers 1010 and 1005, in this example, a pointat the mid-point of the eye line markers of FIG. 9. Thus, astraightforward determination as to whether a secondary marker is withina convex hull, as is the case with the secondary marker 1010 and theconvex hull 1015, but is not the case with the secondary marker 1005 andthe convex hull 1015, may be made. This allows for a concept ofsynchronization in which detecting a face within a moving device'sconfines is sufficient to identify a spoofing attempt. This approach hasthe further advantage that a video playing on the device 220, instead ofa static image, used as the facsimile will not work because it still isframed by the device 220.

FIGS. 11-13 illustrate various techniques in which the user's 115movement against the background provides an indication as to whether ornot the body part is live.

FIG. 11 is a sequence 1100 of an example user interface in which atarget 1115 is presented in frame with the representation 205 of theuser 115, according to an embodiment. A live user 115 moves againsttheir background. In contrast, when an attacker tries to move afacsimile face 215 displayed by the device to simulate movement by theuser 115, the background moves with the facsimile face 215. The system105 may take advantage of this characteristic by making the user 115perform an act that would be difficult to simulate with a static image.For instance, one could ask the user 115 to perform an action such asblinking or head turning.

The system 105, however, may take advantage of the difference between alive scene and a static image by enticing the user 115 to move afterthey are in front of the sensor 120. In an example, the system 105places a target 1115 on the screen in a position that includes a portionof the representation 205 of the user 115, but is off-center from therepresentation 205. The target 1115 entices the user 115 to move so thather face 205 is centered on the target. This allows system 105 to checkthat the feature (e.g., the face, feet, hands, shape of eyes, etc.)being authenticated is moving independently from the background. Themethod is so fluid and natural the user 115 doesn't even feel like theyhad to do anything interactive.

As illustrated, when the user 115 approaches the sensor 120, thesequence of images are captured. The image includes a representation 205of the user 115 and a background image. In a face-based authenticationembodiment, authentication system 105 detects the position of the user's115 face 205 in frame 1105 and projects a target 1115 around, butoff-center from, the representation 205 in frame 1110.

The user 115 is enticed by the placement of the target 1115 to placetheir representation 205 within the target 1115 for authentication. Inan example, the target 1115 is a much smaller view from the sensor 120than what it may actually show, such as 200×200 pixels instead of640×480. In an example, instead of always having this window at thecenter of the frame, it will move to a spot not currently occupied bythe representation 205. The user 115 would then see that they are not inthe frame defined by the target 1115, and would naturally move to thecorrect area. This movement presents the system 105 with enough data toensure that the representation 205 moved but the background did not, asillustrated in frame 1120A. If the background moves as expected for astatic image (that is, with the user 115), the image is a spoof, asillustrated in frame 1120B.

This flow is so natural that the user doesn't even think they are doinganything interactive. There are no words to read, no steps to follow,just a very natural positioning of their face.

FIGS. 12A and 12B are example user interfaces 805 of a target prompt1210 presented to a user in which the scene is obfuscated, according toan embodiment. The user interface 805 includes a display area 1205, avisual target 1210, and a representation of a body part 1215. Asillustrated, the representation of the body part 1215 is not modeledafter the face, or other aspect of the user 115. Rather, the specificnature of the perceived body part is obfuscated to the user 115. Byobfuscating the relationship between the actual image data in thesequence of images from the displayed representation, a malicious userwill have greater difficulty determining how to move a facial facsimilein order to defeat the anti-spoofing technique described herein.

Although it was previously described that the target 1210 is presentedat an offset from the representation 1215 of the user 115, neither thetarget 1210, nor the representation 1215 of the user 115 need hold anyspecial relationship to a position (e.g., center) of the display area1205. FIG. 12A illustrates a target 1210 that approximates therepresentation 1215 of the user 115, while FIG. 12B illustrates anarbitrary shape as a target 1210.

FIG. 13 is an example gaze vector 1315 determination to facilitatetarget placement 1320, according to an embodiment. As illustrated, theuser's face 1310 is not on the target point 1320. The facial orientationis determined as a vector perpendicular to a plane containing a linebetween the user's eyes and centered on that line. The offset 1325 is avector that is the inverse of a component (or all components) of thefacial orientation vector 1315, centered on the target point 1320, andhaving a magnitude determined by the relative motion model to facilitateenough movement by the user 115—performed in attempting to move therepresentation 1215 of the user 115 onto the target 1210—to facilitatethe scene analysis anti-spoofing technique.

FIGS. 14-17 illustrate observing eye blinking and comparing thatobserved blinking against a model to determine whether the correspondingface is live. Existing solutions for liveness detection may use blinkand head movement tracking as means to differentiate a real subject froma spoofed one. Blink detection solutions deployed in current facerecognition products often use binary eye states—opened eye and closedeye—and detect a blink by computing the difference between those twostates. These solutions have several drawbacks. First, these solutionsusually require a user 115 to be still during face capture to try toprevent false blink detections; false blink detections tend to increaseif the user 115 is walking or otherwise moving (such as in or on avehicle). Second, these solutions usually require very good lightingconditions. Third, these solutions usually need very good cameras withexcellent signal-to-noise ratios (“SNRs”). Fourth, these solutionsusually require the user's 115 eyes to open wide; they usually do notwork as well with smaller eyes or with faces at a distance. Finally,these solutions tend to be spoofed easily with image-manipulatedanimations.

In contrast, the example given herein performs successfully with anydigital camera, including the low-quality embedded cameras included inmost low-cost laptops currently on the market. In some examples, SNR isnot important because a sequence of eye movements is tracked instead ofsingle images, reducing the quality needed for any given image. Further,noise present in the sequence, even if the noise is significant, islikely present in each frame of the sequence, and the algorithms factorout this noise.

FIG. 14 is an example of a sequence 1400 of eyelid movements in a blink,according to an embodiment. As the sequence 1400 progresses from frame1405 to 1410 to 1415, the eyes are blinking shut. Frames 1415 to 1420and 1425 illustrate the opening portion of the blinking sequence 1400.

FIG. 15 is a sequence 1500 of frames tracking blink characteristics ofan eye, according to an embodiment. Specifically, the sequence 1500illustrates a typical human blink pattern as an eye transitions from anopened position to a closed position. Original images of the eyes(bottom), as well as binary (e.g., black and white) image versions ofthe eyes (top) are shown for each frame 1505-1530 to illustrate how someembodiments track the eyelid's position in a sequence of frames. Asequence of eyelid positions, such as those illustrated, may be used todetect whether the eyelid in the sequence of frames 1500 transitionsbetween opened-eye (e.g., frame 1505) and closed-eye (e.g., frame 1530)positions in a natural (e.g., normal) or unnatural (e.g., abnormal)manner. In an example, pixel intensity changes are monitored from closedeye (e.g., frame 1530), to partially opened eye (e.g., any of frames1510-1525), and then to fully opened eye (e.g., frame 1505). In anexample, the pixel intensity changes are monitored in reverse order. Inan example, the pixel intensity changes of both orders (e.g., closed eyeto opened eye and opened eye to closed eye) are monitored. In anexample, many (e.g., more than 3) different states of eyelid movementare monitored.

FIG. 16 is an example activity analysis 1600 of a blink for each of twoeyes, according to an embodiment. The activity analysis 1600 illustratesan eyelid sequence in a typical human blink pattern. In the illustratedchart, the x-axis is time and the y-axis is a measure of how open theeyelid is. As illustrated in FIG. 16, a normal blink pattern of a humanfollows a sinusoidal-like wave between the states of opened eye andclosed eye. Furthermore, a normal blink pattern has a steady opened eyeposition prior to a blink, then substantially uniform closing andopening eyelid sequences during the blink. Although the left eye and theright eye move in near synchronicity and produce similar movements, in anormal blink pattern of a human, the left and right eyes usually displayslight variations between each other. Blinks simulated using manipulatedimages and image-manipulated animations usually do not show suchsequences. In an example, a pattern recognizer 1605 operates on an eyeopening movement, an eye closing movement, or both. In an embodimentillustrated in the activity analysis 1600, the pattern recognizer 1605operates on the eye opening movement.

FIG. 17 is an example activity analysis 1700 of a blink for each of twoeyes, according to an embodiment. The activity analysis 1700 illustratesan example abnormal blink pattern observed during a liveness spoofingattack based on an image-manipulated animation. In an example, eyelidmovements that have low amplitudes and or high jitter are detected andflagged as abnormal. In an example, unsynchronized eyelid movements aredetected and flagged as abnormal. In an example, unsynchronized eyelidmovements include eyelids that move in opposite directions.

FIG. 18 illustrates a flow diagram of an example of a method 1800 forspoofing detection in image biometrics, according to an embodiment. Theoperations of the method 1800 are performed on computer hardware, suchas the components described above with respect to FIG. 1 or below withrespect to FIG. 20. As noted above, detecting a spoofing attempt is oneside of a coin in image based biometric authentication; the other sidebeing liveness detection. The method 1800 is directed to ascertainingwhether a face is artificial, or spoofed. However, many of the describedtechniques may be changed slightly to perform a liveness evaluation.

At operation 1805, a sequence of images may be obtained from a camera. Afirst plurality of images in the sequence of images may include arepresentation of a user body part and a representation of anenvironment of the user. In an example, the body part is a face. In anexample, the sequence of images may be processed to reduce noise. In anexample, the sequence of images may be processed to reduce colorinformation. In an example, the color information may be reduced to 1bit per pixel (e.g., black and white). In an example, capturing thesequence of images may be initiated as the used approaches the camera.

At operation 1810, a marker may be created for the representation of thebody part. The marker corresponds to a feature in the representation ofthe body part. In an example, the marker may be a scaling applied to thebody part. In an example, the measurement of the body part based on thedepth measurement and the representation of the body part may bedetermined by extrapolating the size of the body part using the depthimage and dimension of representation of the body part. Such scaling maybe accomplished by taking the pixel representation of the body part inthe sequence of images and transforming it by a perspective model givena distance of the object in the scene from the camera. In an example,the measurement of the body part may include a depth map of the bodypart.

In an example, the marker may be a line drawn between eyes in a face. Inan example, the marker may be a single point at the center-point betweenthe eyes. In an example, the marker may be a measured eye blinkingsequence. In an example, the measured eye blinking sequence may bedetermined by extracting, from each image in the sequence of images inwhich an eye of the user is detected, a respective region of interestcorresponding to each eye of the user, the respective region of interestincluding an eyelid. Then, for each respective region of interest, arespective score corresponding to a percentage of the eye unobstructedby the eyelid may be calculated to create a series of respective scores.

At operation 1815, a feature of the environment of the user presentduring a second plurality of images in the sequence of images may beidentified. In an example, identifying the feature of the environmentmay include emitting a pattern into the environment. The pattern iscomposed of a frequency and an amplitude of light at positions within afield of view of the authenticating camera. Thus, the pattern may beuniform, such as a uniform amplitude of a single wavelength across thefield of view (e.g., a solid pattern) over time, or non-uniform, suchthat at least one of wavelength or amplitude are varied across the fieldof view over time, such as a green star with a white background beingprojected into the field of view. In an example, the non-uniform patternmay be a moving pattern discernable to either the user or to the camera.A moving pattern includes at least one variance in wavelength oramplitude that changes in relation to other emitted features to suggestmovement over time (e.g., the replication of a horizontal black bar onsubsequently greater rows of the raster representation of an image overtime while previous representations of the bar are either not reproducedor covered (e.g., by a background image or color) giving the impressionthat the bar is moving downward).

In an example with a moving, non-uniform pattern, operations of themethod 1800 may optionally include: tracking a gaze of an eye from therepresentation of the body; measuring the degree to which the gazetracks the moving pattern; and providing a second spoofing attemptindication in response to the degree to which the gaze tracks the movingpattern meets a predetermined range. In this example, the user's naturalinclination to track moving objects may be exploited to determine userliveness without an additional procedure or instruction. Thus, such anobservation may be combined with the other spoofing indications torender a more accurate spoofing decision without additional overheadactivity by the user. In an example, the gaze of the eye may bedetermined by measuring the amount of iris visible for the eye. Toensure accurate results, a calibration session may be conducted at somepoint for the user and stored to assess the eyelid positions for theuser to ascertain the gaze of the eye. Such a calibration session mayinclude presenting a moving prompt, capturing a second sequence ofimages during presentation of the moving prompt, measuring acorrespondence of user eyelid positions to the moving prompt from thesecond sequence of images, and using the correspondence as the thresholdfor the user.

In an example, the pattern may be emitted in a light spectrum that isnot visible to the human eye. Such non-visible light may be infrared(IR) or longer wavelength light, or ultraviolet or short wavelengthlight.

In an example, the environmental feature may be a depth measurement ofthe body part derived from the pattern. For example, an IR depth cameramay be used to ascertain depth information for objects in the scene. Inan example a visible light depth calculation, such as noting the scalingof a non-uniform pattern reflected to the camera, may be used to gatherdepth information.

In an example, the feature of the environment may include a reflectivityof the pattern in an area of the environment that contains the markerfor the body part. Such reflectivity may indicate a material or surfaceat that area which may be inconsistent with a non-spoofed body part. Forexample, the human face is partially reflective in the near-infraredwhile most commercial inks and device screens are not. However, glossysurfaces, such as photo paper or devices screens often reflect visiblespectrum light better than a human face.

In an example, the feature of the environment may include a convex hullof points on edges detected in the plurality of the images. Such astructure provides an area enclosed by edges that move togetherthroughout the sequence of images, such as the border of a device ordevice screen. In an example the edges may be compared to a catalog ofdevice edge configurations to determine whether the edges represent aknown device. The specific convex hull of points may then be fit to aphysical configuration of the known device. In an example, only movingedges are used for the edges. That is, non-moving edges between imagesin the sequence of images are ignored.

In an example, the environmental feature is a background proximate tothe body part.

In an example, the environmental feature may be a model of an eyeblinking sequence. Such a model exists prior to the capture of thesequence of images.

At operation 1820, a correlation between the marker and the feature ofthe environment in the sequence of images may be quantified to produce asynchronicity metric of the degree to which the marker and the featureof the environment correlate. In an example, where the marker is ascaling of the body part and the environmental feature is a distancefrom the camera to the body part, the correlation may be the degree towhich the measurement of the body part conforms to a live body part atthe depth measurement. Thus, it may be ascertained whether the body partis too small to be from a valid human, such as would be the result if arepresentation in a phone display, or a small replica of the person wereused in a spoofed authentication attempt.

In an example, the correlation between the marker and the feature of theenvironment and the marker may be the degree to which the area reflectsthe pattern. As noted above, such a correlation may provide insight intoa material provide the body part representation in the sequence ofimages. In an example, the homogeneity of the pattern may be used tocorrelative effect. For example, the homogenous absence of IR lightreflected from device screens and the heterogeneous IR reflection fromhuman faces may be the basis for correlating the body marker to theenvironmental feature.

In examples where the environmental feature is a convex hull of points,the correlation between the marker and the convex hull is the degree towhich the marker is located within the convex hull.

In an examples where the feature of the environment is a backgroundproximate to the body-part, the correlation may be the degree to whichthe proximate background moves with the marker. Thus, if a marker, movesacross the sequence of images from right to left, and a background, suchas an edge, mirrors the marker's movement, change in orientation, etc.,there would be a highly correlative movement between the two components.Thus, the synchronicity metric is the degree to which these movementsactually correlate. To facilitate measuring the correlative movements ofthese components, it may help to induce the user to move. To this end, atarget may be displayed to the user along with a representation of thebody part. The user is not provided specific instruction, but may ratherinstinctively attempt to move the body part to the target. Thus, thismechanism induces some user movement, but the movement is a degree ofmovement and the specific type of movement (e.g., assuming a set ofscripted positions) is not relied upon.

In an example, the target may be an outline of a shape sized such thatthe representation of the body part fits inside the shape when a centerof the shape coincides with a center of the representation of the bodypart. In an example, the shape may be a rectangle. In an example, thetarget may be a shape sized such that the representation of the bodypart does not fit inside the shape when a center of the shape coincideswith a center of the representation of the body part. In an example, therepresentation of the body part may be symbol obfuscating at least onefeature of the body part from the sequence of images. In an example, thesymbol is an illustration of the body part.

In an example, the target may be displayed at an offset from the bodypart. In an example, the offset may be determined by a relative motionmodel defining a motion parameter of the body part to distinguishbetween foreground objects and background objects in a field of view ofa camera capturing the sequence of images. In an example, where the bodypart is a face, the direction of the offset is the inverse of anorientation of the face. In an example, the distance of the offset fromthe representation of the body part may not be coupled to a pixelposition difference between the body part captured in the sequence ofimages and the target. That is, the offset need not reflect the realityof the difference between the body part in the sequence of images andthe area filled by the target. In an example, the relative motion modelincludes a threshold of movement sufficient to distinguish foregroundobjects from background objects. In an example, displaying therepresentation of the body part may include modifying the motion of thebody part using the relative motion model. Such modifications mayinclude speeding up or slowing down the movement at various pointsthroughout the moving. Thus, the user may traverse the entire image fromleft to right which the display still indicates the body part being leftof the target.

In an example, the correlation is the degree to which the measured eyeblinking sequence marker described above conforms to the eye blinkingenvironmental model. Such a correlation addresses inaccurate attempts toanimate a still picture used for spoofing to simulate blinking. In anexample where the eye blinking sequence is determined by calculating,for each respective region of interest, a respective score correspondingto a percentage of the eye unobstructed by the eyelid to create a seriesof respective scores, the degree to which the measured eye blinkingsequence conforms to the model may be determined by processing, througha pattern recognizer, the series of respective scores. The patternrecognizer may check the series of respective scores for at least one ofan abnormal eyelid sequence or an abnormal blink sequence based on theeye blinking model. In an example, the pattern recognizer may seriallycheck the abnormal blink sequence after verifying that the eyelidsequence is normal. In an example, the model defines an eyelid sequencecorresponding to eyelids that move in opposite directions as an abnormaleyelid sequence.

At operation 1825, a spoofing attempt indication may be provided inresponse to the synchronicity metric being beyond a threshold. In anexample, an authentication process may deny authenticating the user inresponse to the spoofing attempt indication. In an example, wherein thecorrelation is based on emitted light reflectivity, the threshold maydefine a minimum reflectivity of the pattern. Thus, a spoofing attemptis indicated where the reflected pattern is above the threshold. Such isthe case in a visible light pattern reflected from a device screen. Suchreflection is unlikely to occur from a human face. In an example, theminimum reflectivity of the pattern may include a minimum reflectivityof a specified feature of the pattern. For example, if a high contrastimage of a star were emitted, a faithful reproduction may be expected tobe reflected off of a flat surface, such as a screen, and not a humanface, even if a reflection from the face is discerned. In an example,the pattern is moving and the degree to which the area reflects thepatter may include determining whether the specified feature of thepattern moved an expected distance.

In an example, the threshold may define a maximum reflectivity of thepattern. In such a case, the expectation is that the emitted light willreflect better off of the true body part than it will off of thefacsimile, as happens with IR light. Thus, a spoofing attempt faceoccurs in an area with little to no IR reflection.

In examples where the user is encouraged to move via a target, and thecorrelation producing the synchronicity metric is the movement of themarker and a proximate background, the spoofing indication may beprovided prior to the marker reaching the target. Thus, as explainedabove, the user need not actually reach the target, but simply moveenough to determine relative movement of foreground and backgroundobjects. When the foreground and background objects move together, aspoofing attempt has likely been attempted.

FIG. 19 illustrates a flow diagram of an example of a method 1900 forfacial liveness detection in image biometrics, according to anembodiment. The operations of the method 1900 are performed on computerhardware, such as the components described above with respect to FIG. 1or below with respect to FIG. 20. As noted above, detecting a livenessdetection is one side of a coin in image based biometric authentication;the other side being spoofing attempt detection. The method 1900 isdirected to ascertaining whether a face or other body part is live, asopposed to a facsimile. However, many of the described techniques may bechanged slightly to perform a spoofing attempt evaluation.

At operation 1905, a set of potential authentication faces may bedetected in an images sequence captured during an authentication attemptby a user.

At operation 1910, an iterative process is applied to the detected facesto determine which are live and which are not.

At decision 1915, if there are no more new faces (e.g., each face in theset is processed once), the method 1900 proceeds to operation 1935.

At operation 1920, a set of liveness model tests is applied to thecurrent face. As noted above, more than one test may be used to provideinsight into the liveness of the face. Each test in the set of livenesstests may include an assay (e.g., test) that differs between a live faceand a face simulated on a pictorial representation of a face. That is,the assay distinguishes between a true face and a facsimile based on atleast one model of a live face. For example, human faces may have anacceptable size range determined empirically. A facsimile present on apocket-sized screen is probably much smaller than the acceptable sizerange. Thus, the assay tests the size of the face to determine whetherit is within the acceptable size ranger. Further, the assay operatesindependently of body arrangement instructions given to the user. Thatis, the assay does not use, nor rely, on giving the user instructions,such as blinking on command, positioning the arms in a particularmanner, etc. Although the assay may use prompts, the user is given nospecific instruction as to what to do with the prompt. Thus, the user isunburdened by any particular script when authenticating.

In an example, a test in the liveness model may include emitting lighttowards the face with an emitter. The test may also include measuring aninteraction of the light with the face. The test may further includecomparing the interaction with a model of a live face. Tests that useemitted light may provide a number of useful measurements to be used bythe test's assay. In an example, the interaction of the light with theface provides a distance measurement between the face and the emitter.In this example, the model of the live face may include an acceptablesize range of a live face. Thus, comparing the interaction with themodel may include scaling a visual light spectrum representation of theface acquired from the images sequence (e.g., the captured picture ofthe face) using the distance measurement to determine whether the faceis within the acceptable size range of a live face.

Another useful measurement from emitted light is the reflectivity of thevarious scene surfaces with various types of light. In an example, theinteraction of the light with the face may be a reflectivity measurementof the character of the light reflected from the face. Lightcharacteristics may include amplitude or wavelength over an area as wellas time. In an example, emitting the light may include emittingnon-visual spectrum light. In this example, the model of a live face mayinclude a non-uniform positive reflectivity of the light. That is, thelive face reflects a detectable amount of the non-visible light.Moreover, the reflected light is non-uniform (uniform reflectionsuggesting a flat surface inconsistent with a human face). Thus,comparing the interaction with the model of the face may includedetermining whether the area in which the face was found has non-uniformpositive reflectivity in the non-visual spectrum light. In an example,the non-visual spectrum light is IR light.

In an example, emitting the light may include emitting a non-uniformpattern of visible light. As noted above, a non-uniform pattern variesin at least one of wavelength or amplitude over an area (such as thatshown in FIG. 5A). In this example, the model of a live face does notreflect, or poorly reflects, the pattern. A poor pattern reflection maybe addressed by adjusting the threshold (e.g., a filter) over whichreflectivity is counted and thus partial reflectivity below thethreshold may be treated like a non-reflective surface. Also in thisexample, comparing the interaction with the model of the face mayinclude determining whether the pattern is discernable over the face.For example, a non-reflective human face should not be represented inthe sequence of images with an overlay of the pattern, whereas areflective surface of a device will display such a pattern, such as isillustrated in FIG. 5B.

In an example, a test in the liveness model of tests may includeproviding a target, without an instruction to the user, on a display.Such a target may provoke the user to move in a particular way, but doesnot require adherence to a script or other instructed machination toprove liveness. For example, a moving target will likely cause aninstinctual response for the user to follow the target with their eyes.Similarly, presenting an outline of a face transposed from therepresentation of the user's face will likely prompt an attempt by theuser to put their face within the outline. In an example, the target ismoving. In this example, the test from the set of liveness model testsmay include tracking an eye gaze of the user with respect to the target.Also, in this example, the assay is an evaluation of how closely the eyegaze tracks the target. Here, a natural human reaction to a stimulus isused to differentiate between areal face and a facsimile.

In an example, the target is a frame superimposed on the sequence ofimages re-displayed to the user. Thus, the user sees themselves and anartificial frame in the display. In this example, the test from the setof liveness model tests includes determining a synchronous movementbetween the user and an environmental feature ascertainable from thesequence of images. Thus, liveness is determined by independence of theface with respect to its surroundings. A facsimile face, for example,will likely have artifacts form the environment that move with it, suchas background imagery, device borders, etc. In contrast, a live facewill not have such artifacts. Thus, in this example, the assay is anevaluation of how closely movement of the environmental feature issynchronized to movement of the user. The target induces the user tomove until enough movement has occurred to determine whether the user isdecoupled from the environmental features enough to determine liveness.

In an example, the target may be an icon in a display area including anavatar of the user. In this example, the display area does not includethe sequence of images, but rather obfuscates the environment of theauthentication. Such obfuscation permits manipulation of the relativemotion or position of measurable elements to complicate a spoofingattack. In this example, the test from the set of liveness model testsmay include determining a synchronous movement between the user and anenvironmental feature ascertainable from the sequence of images.Further, the assay may be an evaluation of how closely movement of theenvironmental feature is synchronized to movement of the user.

In an example, a test in the liveness model of tests may includeidentifying an outline conforming to a device across the sequence ofimages. In this example, the assay may be whether the face appearswithin the outline across a plurality of the sequence of images. Thus,if the face is ensconced in the device outlines throughout the video, itmay be determined that the device is generating the face. Accordingly, alive face does not occur within the device edge outline.

At operation 1925, the current face may be added to a set ofauthentication faces when the result of the set of liveness model testsmeets a threshold. In an example, the set of authentication faces may bemaintained as a separate data structure, or may be a marking orrecording of the faces selected to be authentication faces. In anexample, whether the face appears within the outline across theplurality of the sequence of images is true if the face appears in anarea of an image in the sequence of images and the outline encloses thearea in a set of preceding images in the sequence of images. Thisprovision captures the reality that low-quality lighting or imagecapture may lead to frames in which the device outline is notdiscernable but the face is, or vice versa. If a frame occurs in whichthe device outline is not discernable, but a previous frame had adiscernable device outline, the device outline may be inferred in theframe. In an example, the set of preceding images may be selected basedon a movement threshold applied to the outline. Thus, a velocity may becalculated for an outline moving across the field of view. If thevelocity indicates movement, then a previous outline location would notbe used as an inferred device outline when determining if the faceappears within the outline. Accordingly, the movement threshold denotesthe degree to which the outline is moving in order to ascertain in whatarea of an image the outline may be inferred.

At operation 1930, a next face in the set of potential authenticationfaces is selected as the current face for analysis. The operation 1930may be performed as part of decision 1915, prior to operation 1920, orafter operation 1925 as illustrated here.

At operation 1935, after the set of authentication faces is populated,facial authentication may be applied to faces in the set ofauthentication faces. In an example, the operation 1935 may be performedas each current face is identified as live, instead of waiting until allfaces have been processed.

FIG. 20 illustrates a block diagram of an example machine 2000 uponwhich any one or more of the techniques (e.g., methodologies) discussedherein may perform. In alternative embodiments, the machine 2000 mayoperate as a standalone device or may be connected (e.g., networked) toother machines. In a networked deployment, the machine 2000 may operatein the capacity of a server machine, a client machine, or both inserver-client network environments. In an example, the machine 2000 mayact as a peer machine in peer-to-peer (P2P) (or other distributed)network environment. The machine 2000 may be a personal computer (PC), atablet PC, a set-top box (STB), a personal digital assistant (PDA), amobile telephone, a web appliance, a network router, switch or bridge,or any machine capable of executing instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein, such as cloudcomputing, software as a service (SaaS), other computer clusterconfigurations.

Examples, as described herein, may include, or may operate by, logic ora number of components, or mechanisms. Circuit sets are a collection ofcircuits implemented in tangible entities that include hardware (e.g.,simple circuits, gates, logic, etc.). Circuit set membership may beflexible over time and underlying hardware variability. Circuit setsinclude members that may, alone or in combination, perform specifiedoperations when operating. In an example, hardware of the circuit setmay be immutably designed to carry out a specific operation (e.g.,hardwired). In an example, the hardware of the circuit set may includevariably connected physical components (e.g., execution units,transistors, simple circuits, etc.) including a computer readable mediumphysically modified (e.g., magnetically, electrically, moveableplacement of invariant massed particles, etc.) to encode instructions ofthe specific operation. In connecting the physical components, theunderlying electrical properties of a hardware constituent are changed,for example, from an insulator to a conductor or vice versa. Theinstructions enable embedded hardware (e.g., the execution units or aloading mechanism) to create members of the circuit set in hardware viathe variable connections to carry out portions of the specific operationwhen in operation. Accordingly, the computer readable medium iscommunicatively coupled to the other components of the circuit setmember when the device is operating. In an example, any of the physicalcomponents may be used in more than one member of more than one circuitset. For example, under operation, execution units may be used in afirst circuit of a first circuit set at one point in time and reused bya second circuit in the first circuit set, or by a third circuit in asecond circuit set at a different time.

Machine (e.g., computer system) 2000 may include a hardware processor2002 (e.g., a central processing unit (CPU), a graphics processing unit(GPU), a hardware processor core, or any combination thereof), a mainmemory 2004 and a static memory 2006, some or all of which maycommunicate with each other via an interlink (e.g., bus) 2008. Themachine 2000 may further include a display unit 2010, an alphanumericinput device 2012 (e.g., a keyboard), and a user interface (UI)navigation device 2014 (e.g., a mouse). In an example, the display unit2010, input device 2012 and UI navigation device 2014 may be a touchscreen display. The machine 2000 may additionally include a storagedevice (e.g., drive unit) 2016, a signal generation device 2018 (e.g., aspeaker), a network interface device 2020, and one or more sensors 2021,such as a global positioning system (GPS) sensor, compass,accelerometer, or other sensor. The machine 2000 may include an outputcontroller 2028, such as a serial (e.g., universal serial bus (USB),parallel, or other wired or wireless (e.g., infrared (IR), near fieldcommunication (NFC), etc.) connection to communicate or control one ormore peripheral devices (e.g., a printer, card reader, etc.).

The storage device 2016 may include a machine readable medium 2022 onwhich is stored one or more sets of data structures or instructions 2024(e.g., software) embodying or utilized by any one or more of thetechniques or functions described herein. The instructions 2024 may alsoreside, completely or at least partially, within the main memory 2004,within static memory 2006, or within the hardware processor 2002 duringexecution thereof by the machine 2000. In an example, one or anycombination of the hardware processor 2002, the main memory 2004, thestatic memory 2006, or the storage device 2016 may constitute machinereadable media.

While the machine readable medium 2022 is illustrated as a singlemedium, the term “machine readable medium” may include a single mediumor multiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) configured to store the one or moreinstructions 2024.

The term “machine readable medium” may include any medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine 2000 and that cause the machine 2000 to perform any one ormore of the techniques of the present disclosure, or that is capable ofstoring, encoding or carrying data structures used by or associated withsuch instructions. Non-limiting machine readable medium examples mayinclude solid-state memories, and optical and magnetic media. In anexample, a massed machine readable medium comprises a machine readablemedium with a plurality of particles having invariant (e.g., rest) mass.Accordingly, massed machine-readable media are not transitorypropagating signals. Specific examples of massed machine readable mediamay include: non-volatile memory, such as semiconductor memory devices(e.g., Electrically Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM)) and flash memorydevices; magnetic disks, such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 2024 may further be transmitted or received over acommunications network 2026 using a transmission medium via the networkinterface device 2020 utilizing any one of a number of transferprotocols (e.g., frame relay, internet protocol (IP), transmissioncontrol protocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP), etc.). Example communication networks may include alocal area network (LAN), a wide area network (WAN), a packet datanetwork (e.g., the Internet), mobile telephone networks (e.g., cellularnetworks), Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., Institute of Electrical and Electronics Engineers (IEEE)802.11 family of standards known as Wi-Fi®, IEEE 802.16 family ofstandards known as WiMax®), IEEE 802.15.4 family of standards,peer-to-peer (P2P) networks, among others. In an example, the networkinterface device 2020 may include one or more physical jacks (e.g.,Ethernet, coaxial, or phone jacks) or one or more antennas to connect tothe communications network 2026. In an example, the network interfacedevice 2020 may include a plurality of antennas to wirelesslycommunicate using at least one of single-input multiple-output (SIMO),multiple-input multiple-output (MIMO), or multiple-input single-output(MISO) techniques. The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions for execution by the machine 2000, and includesdigital or analog communications signals or other intangible medium tofacilitate communication of such software.

ADDITIONAL NOTES & EXAMPLES

Example 1 includes subject matter for spoofing detection in imagebiometrics (such as a method, means for performing acts, machinereadable medium including instructions that when performed by a machinecause the machine to performs acts, or an apparatus to perform)comprising: a sensor to obtain a sequences of images, a first pluralityof images in the sequence of images including a representation of a userbody part, and a second plurality of images in the sequence of imagesincluding a representation of an environment of the user, the sensorbeing a camera; a biometric feature detector to create a marker for therepresentation of the body part, the marker corresponding to a featurein the representation of the body part; an environmental featuredetector to identify a feature of the environment of the user presentduring the second plurality of images in the sequence of images; asynchronicity detector to quantify a correlation between the marker andthe feature of the environment in the sequence of images to produce asynchronicity metric of the degree to which the marker and the featureof the environment correlate; and a spoofing indication controller toprovide a spoofing attempt indication in response to the synchronicitymetric being beyond a threshold.

In Example 2, the subject matter of Example 1 may include, wherein thebody part is a face.

In Example 3, the subject matter of any one of Examples 1 to 2 mayinclude, wherein the system is to process the sequence of images toreduce noise.

In Example 4, the subject matter of any one of Examples 1 to 3 mayinclude, wherein the system is to process the sequence of images toreduce color information.

In Example 5, the subject matter of any one of Examples 1 to 4 mayinclude, wherein the color information is reduced to one bit per pixel.

In Example 6, the subject matter of any one of Examples 1 to 5 mayinclude, wherein the sensor is to capture the sequence of images with acamera as the user approaches the sensor.

In Example 7, the subject matter of any one of Examples 1 to 6 mayinclude, an authentication controller to deny user authentication inresponse to the spoofing attempt indication.

In Example 8, the subject matter of any one of Examples 1 to 7 mayinclude, wherein to identify the feature of the environment includes anemitter of the system to emit a pattern into the environment.

In Example 9, the subject matter of any one of Examples 1 to 8 mayinclude, wherein to emit the pattern includes the emitter to emit anon-uniform pattern in a visible light spectrum.

In Example 10, the subject matter of any one of Examples 1 to 9 mayinclude, wherein the non-uniform pattern is a moving pattern.

In Example 11, the subject matter of any one of Examples 1 to 10 mayinclude, wherein: the biometric feature detector is to track a gaze ofan eye from the representation of the body part; the synchronicitydetector is to measure the degree to which the gaze tracks the movingpattern; and the spoofing indication controller is to provide a secondspoofing attempt indication in response to the degree to which the gazetracks the moving pattern meets a predetermined range.

In Example 12, the subject matter of any one of Examples 1 to 11 mayinclude, the system is to perform a calibration session presenting amoving prompt; capturing a second sequence of images during presentationof the moving prompt; measuring a correspondence of user eyelidpositions to the moving prompt from the second sequence of images; usingthe correspondence as the threshold.

In Example 13, the subject matter of any one of Examples 1 to 12 mayinclude, wherein the gaze of the eye is determined by measuring theamount of iris visible for the eye.

In Example 14, the subject matter of any one of Examples 1 to 13 mayinclude, wherein to emit the pattern includes the emitter to emit thepattern in a non-visible light spectrum.

In Example 15, the subject matter of any one of Examples 1 to 14 mayinclude, wherein the feature of the environment is a depth measurementof the body part derived from the pattern, wherein the marker is ameasurement of the body part based on the depth measurement and therepresentation of the body part, wherein the correlation is the degreeto which the measurement of the body part conforms to a live body partat the depth measurement.

In Example 16, the subject matter of any one of Examples 1 to 15 mayinclude, wherein the measurement of the body part based on the depthmeasurement and the representation of the body part is determined byextrapolating the size of the body part using the depth image and adimension of representation of the body part.

In Example 17, the subject matter of any one of Examples 1 to 16 mayinclude, wherein the measurement of the body part includes a depth mapof the body part.

In Example 18, the subject matter of any one of Examples 1 to 17 mayinclude, wherein the feature of the environment includes a reflectivityof the pattern in an area of the environment containing the marker.

In Example 19, the subject matter of any one of Examples 1 to 18 mayinclude, wherein the correlation between the feature of the environmentand the marker is the degree to which the area reflects the pattern.

In Example 20, the subject matter of any one of Examples 1 to 19 mayinclude, wherein the threshold defines a minimum reflectivity of thepattern.

In Example 21, the subject matter of any one of Examples 1 to 20 mayinclude, wherein the minimum reflectivity of the pattern includes aminimum reflectivity of a specified feature of the pattern.

In Example 22, the subject matter of any one of Examples 1 to 21 mayinclude, wherein the pattern is moving, and wherein the degree to whichthe area reflects the patter includes determining whether the specifiedfeature of the pattern moved an expected distance.

In Example 23, the subject matter of any one of Examples 1 to 22 mayinclude, wherein the threshold defines a maximum reflectivity of thepattern.

In Example 24, the subject matter of any one of Examples 1 to 23 mayinclude, wherein to emit the pattern includes the emitter to emit thepattern in a non-visible light spectrum, wherein the degree to which thearea reflects the pattern includes the homogeneity of reflection of thenon-visible light.

In Example 25, the subject matter of any one of Examples 1 to 24 mayinclude, wherein the degree to which the area reflects the patternincludes a brightness of the reflection of non-visible light, a brightreflection corresponding to a high reflectivity.

In Example 26, the subject matter of any one of Examples 1 to 25 mayinclude, wherein the feature of the environment includes a convex hullof points on edges detected in the plurality of the images.

In Example 27, the subject matter of any one of Examples 1 to 26 mayinclude, wherein the edges are compared to a catalog of device edgeconfigurations to determine whether the edges represent a known device,and wherein the convex hull of points is based on a physicalconfiguration of the known device.

In Example 28, the subject matter of any one of Examples 1 to 27 mayinclude, wherein the edges are moving edges.

In Example 29, the subject matter of any one of Examples 1 to 28 mayinclude, wherein the correlation between the marker and the feature ofthe environment is the degree to which marker is found within the convexhull.

In Example 30, the subject matter of any one of Examples 1 to 29 mayinclude, wherein the marker is a line between eyes in the representationof the body part.

In Example 31, the subject matter of any one of Examples 1 to 30 mayinclude, wherein the feature of the environment is a backgroundproximate to the body part, and wherein the correlation is the degree towhich the background proximate to the body part moves with the marker.

In Example 32, the subject matter of any one of Examples 1 to 31 mayinclude, a display, and wherein the system is to display, on thedisplay, a target and the representation of the body part to the user,the target being in a different position than the representation of thebody part.

In Example 33, the subject matter of any one of Examples 1 to 32 mayinclude, wherein the target is an outline of a shape sized such that therepresentation of the body part fits inside the shape when a center ofthe shape coincides with a center of the representation of the bodypart.

In Example 34, the subject matter of any one of Examples 1 to 33 mayinclude, wherein the shape is a rectangle.

In Example 35, the subject matter of any one of Examples 1 to 34 mayinclude, wherein the target is a shape sized such that therepresentation of the body part does not fit inside the shape when acenter of the shape coincides with a center of the representation of thebody part.

In Example 36, the subject matter of any one of Examples 1 to 35 mayinclude, wherein the target being in a different position than therepresentation of the body part includes displaying the target at anoffset from the body part, the offset determined by a relative motionmodel defining a motion parameter of the body part to distinguishbetween foreground objects and background objects in a field of view ofthe sensor capturing the sequence of images.

In Example 37, the subject matter of any one of Examples 1 to 36 mayinclude, wherein the body part is a face, and wherein the direction ofthe offset is the inverse of an orientation of the face.

In Example 38, the subject matter of any one of Examples 1 to 37 mayinclude, wherein the distance of the offset from the representation ofthe body part is not coupled to a pixel position difference between thebody part captured in the sequence of images and the target.

In Example 39, the subject matter of any one of Examples 1 to 38 mayinclude, wherein the relative motion model includes a threshold ofmovement sufficient to distinguish foreground objects from backgroundobjects.

In Example 40, the subject matter of any one of Examples 1 to 39 mayinclude, wherein to display the representation of the body part includesthe system to modify the motion of the body part using the relativemotion model, speeding up or slowing down the movement at various pointsthroughout the moving.

In Example 41, the subject matter of any one of Examples 1 to 40 mayinclude, wherein the spoofing attempt indication is made prior to therepresentation of the body part reaching the target.

In Example 42, the subject matter of any one of Examples 1 to 41 mayinclude, wherein the representation of the body part is symbolobfuscating at least one feature of the body part from the sequence ofimages.

In Example 43, the subject matter of any one of Examples 1 to 42 mayinclude, wherein the symbol is an illustration of the body part.

In Example 44, the subject matter of any one of Examples 1 to 43 mayinclude, wherein the environmental feature is a model of an eye blinkingsequence, wherein the marker is a measured eye blinking sequence, andwherein the correlation is the degree to which the measured eye blinkingsequence conforms to the model.

In Example 45, the subject matter of any one of Examples 1 to 44 mayinclude, wherein the measured eye blinking sequence is determined by thebiometric feature detector, the biometric feature detector to: extract,from each image in the sequence of images in which an eye of the user isdetected, a respective region of interest corresponding to each eye ofthe user, the respective region of interest including an eyelid; andcalculate, for each respective region of interest, a respective scorecorresponding to a percentage of the eye unobstructed by the eyelid tocreate a series of respective scores; and wherein the degree to whichthe measured eye blinking sequence conforms to the model is determinedby processing, through a pattern recognizer, the series of respectivescores, the pattern recognizer checking the series of respective scoresfor at least one of an abnormal eyelid sequence or an abnormal blinksequence based on the model.

In Example 46, the subject matter of any one of Examples 1 to 45 mayinclude, wherein the pattern recognizer serially checks the abnormalblink sequence after verifying that the eyelid sequence is normal.

In Example 47, the subject matter of any one of Examples 1 to 46 mayinclude, wherein the model defines an eyelid sequence corresponding toeyelids that move in opposite directions as an abnormal eyelid sequence.

Example 48 includes subject matter for spoofing detection in imagebiometrics (such as a method, means for performing acts, machinereadable medium including instructions that when performed by a machinecause the machine to performs acts, or an apparatus to perform)comprising: obtaining a sequences of images from a camera using a firstgroup of circuits, a first plurality of images in the sequence of imagesincluding a representation of a user body part, and a second pluralityof images in the sequence of images including a representation of anenvironment of the user; creating a marker for the representation of thebody part using a second group of circuits, the marker corresponding toa feature in the representation of the body part; identifying a featureof the environment of the user present during the second plurality ofimages in the sequence of images using a third group of circuits;quantifying, using a third group of circuits, a correlation between themarker and the feature of the environment in the sequence of images toproduce a synchronicity metric of the degree to which the marker and thefeature of the environment correlate; and providing, using a fourthgroup of circuits, a spoofing attempt indication in response to thesynchronicity metric being beyond a threshold.

In Example 49, the subject matter of Example 48 may include, wherein thebody part is a face.

In Example 50, the subject matter of any one of Examples 48 to 49 mayinclude, processing the sequence of images to reduce noise.

In Example 51, the subject matter of any one of Examples 48 to 50 mayinclude, processing the sequence of images to reduce color information.

In Example 52, the subject matter of any one of Examples 48 to 51 mayinclude, wherein the color information is reduced to one bit per pixel.

In Example 53, the subject matter of any one of Examples 48 to 52 mayinclude, capturing the sequence of images with a camera as the userapproaches the camera.

In Example 54, the subject matter of any one of Examples 48 to 53 mayinclude, denying user authentication in response to the spoofing attemptindication.

In Example 55, the subject matter of any one of Examples 48 to 54 mayinclude, wherein identifying the feature of the environment includesemitting a pattern into the environment.

In Example 56, the subject matter of any one of Examples 48 to 55 mayinclude, wherein emitting the pattern includes emitting a non-uniformpattern in a visible light spectrum.

In Example 57, the subject matter of any one of Examples 48 to 56 mayinclude, wherein the non-uniform pattern is a moving pattern.

In Example 58, the subject matter of any one of Examples 48 to 57 mayinclude, tracking a gaze of an eye from the representation of the bodypart; measuring the degree to which the gaze tracks the moving pattern;and providing a second spoofing attempt indication in response to thedegree to which the gaze tracks the moving pattern meets a predeterminedrange.

In Example 59, the subject matter of any one of Examples 48 to 58 mayinclude, a calibration session presenting a moving prompt; capturing asecond sequence of images during presentation of the moving prompt;measuring a correspondence of user eyelid positions to the moving promptfrom the second sequence of images; using the correspondence as thethreshold.

In Example 60, the subject matter of any one of Examples 48 to 59 mayinclude, wherein the gaze of the eye is determined by measuring theamount of iris visible for the eye.

In Example 61, the subject matter of any one of Examples 48 to 60 mayinclude, wherein emitting the pattern includes emitting the pattern in anon-visible light spectrum.

In Example 62, the subject matter of any one of Examples 48 to 61 mayinclude, wherein the feature of the environment is a depth measurementof the body part derived from the pattern, wherein the marker is ameasurement of the body part based on the depth measurement and therepresentation of the body part, wherein the correlation is the degreeto which the measurement of the body part conforms to a live body partat the depth measurement.

In Example 63, the subject matter of any one of Examples 48 to 62 mayinclude, wherein the measurement of the body part based on the depthmeasurement and the representation of the body part is determined byextrapolating the size of the body part using the depth image and adimension of representation of the body part.

In Example 64, the subject matter of any one of Examples 48 to 63 mayinclude, wherein the measurement of the body part includes a depth mapof the body part.

In Example 65, the subject matter of any one of Examples 48 to 64 mayinclude, wherein the feature of the environment includes a reflectivityof the pattern in an area of the environment containing the marker.

In Example 66, the subject matter of any one of Examples 48 to 65 mayinclude, wherein the correlation between the feature of the environmentand the marker is the degree to which the area reflects the pattern.

In Example 67, the subject matter of any one of Examples 48 to 66 mayinclude, wherein the threshold defines a minimum reflectivity of thepattern.

In Example 68, the subject matter of any one of Examples 48 to 67 mayinclude, wherein the minimum reflectivity of the pattern includes aminimum reflectivity of a specified feature of the pattern.

In Example 69, the subject matter of any one of Examples 48 to 68 mayinclude, wherein the pattern is moving, and wherein the degree to whichthe area reflects the patter includes determining whether the specifiedfeature of the pattern moved an expected distance.

In Example 70, the subject matter of any one of Examples 48 to 69 mayinclude, wherein the threshold defines a maximum reflectivity of thepattern.

In Example 71, the subject matter of any one of Examples 48 to 70 mayinclude, wherein emitting the pattern includes emitting the pattern in anon-visible light spectrum, wherein the degree to which the areareflects the pattern includes the homogeneity of reflection of thenon-visible light.

In Example 72, the subject matter of any one of Examples 48 to 71 mayinclude, wherein the degree to which the area reflects the patternincludes a brightness of the reflection of non-visible light, a brightreflection corresponding to a high reflectivity.

In Example 73, the subject matter of any one of Examples 48 to 72 mayinclude, wherein the feature of the environment includes a convex hullof points on edges detected in the plurality of the images.

In Example 74, the subject matter of any one of Examples 48 to 73 mayinclude, wherein the edges are compared to a catalog of device edgeconfigurations to determine whether the edges represent a known device,and wherein the convex hull of points is based on a physicalconfiguration of the known device.

In Example 75, the subject matter of any one of Examples 48 to 74 mayinclude, wherein the edges are moving edges.

In Example 76, the subject matter of any one of Examples 48 to 75 mayinclude, wherein the correlation between the marker and the feature ofthe environment is the degree to which marker is found within the convexhull.

In Example 77, the subject matter of any one of Examples 48 to 76 mayinclude, wherein the marker is a line between eyes in the representationof the body part.

In Example 78, the subject matter of any one of Examples 48 to 77 mayinclude, wherein the feature of the environment is a backgroundproximate to the body part, and wherein the correlation is the degree towhich the background proximate to the body part moves with the marker.

In Example 79, the subject matter of any one of Examples 48 to 78 mayinclude, displaying a target and the representation of the body part tothe user, the target being in a different position than therepresentation of the body part.

In Example 80, the subject matter of any one of Examples 48 to 79 mayinclude, wherein the target is an outline of a shape sized such that therepresentation of the body part fits inside the shape when a center ofthe shape coincides with a center of the representation of the bodypart.

In Example 81, the subject matter of any one of Examples 48 to 80 mayinclude, wherein the shape is a rectangle.

In Example 82, the subject matter of any one of Examples 48 to 81 mayinclude, wherein the target is a shape sized such that therepresentation of the body part does not fit inside the shape when acenter of the shape coincides with a center of the representation of thebody part.

In Example 83, the subject matter of any one of Examples 48 to 82 mayinclude, wherein the target being in a different position than therepresentation of the body part includes displaying the target at anoffset from the body part, the offset determined by a relative motionmodel defining a motion parameter of the body part to distinguishbetween foreground objects and background objects in a field of view ofa camera capturing the sequence of images.

In Example 84, the subject matter of any one of Examples 48 to 83 mayinclude, wherein the body part is a face, and wherein the direction ofthe offset is the inverse of an orientation of the face.

In Example 85, the subject matter of any one of Examples 48 to 84 mayinclude, wherein the distance of the offset from the representation ofthe body part is not coupled to a pixel position difference between thebody part captured in the sequence of images and the target.

In Example 86, the subject matter of any one of Examples 48 to 85 mayinclude, wherein the relative motion model includes a threshold ofmovement sufficient to distinguish foreground objects from backgroundobjects.

In Example 87, the subject matter of any one of Examples 48 to 86 mayinclude, wherein displaying the representation of the body part includesmodifying the motion of the body part using the relative motion model,speeding up or slowing down the movement at various points throughoutthe moving.

In Example 88, the subject matter of any one of Examples 48 to 87 mayinclude, wherein the spoofing attempt indication is made prior to therepresentation of the body part reaching the target.

In Example 89, the subject matter of any one of Examples 48 to 88 mayinclude, wherein the representation of the body part is symbolobfuscating at least one feature of the body part from the sequence ofimages.

In Example 90, the subject matter of any one of Examples 48 to 89 mayinclude, wherein the symbol is an illustration of the body part.

In Example 91, the subject matter of any one of Examples 48 to 90 mayinclude, wherein the environmental feature is a model of an eye blinkingsequence, wherein the marker is a measured eye blinking sequence, andwherein the correlation is the degree to which the measured eye blinkingsequence conforms to the model.

In Example 92, the subject matter of any one of Examples 48 to 91 mayinclude, wherein the measured eye blinking sequence is determined by:extracting, from each image in the sequence of images in which an eye ofthe user is detected, a respective region of interest corresponding toeach eye of the user, the respective region of interest including aneyelid; and calculating, for each respective region of interest, arespective score corresponding to a percentage of the eye unobstructedby the eyelid to create a series of respective scores; and wherein thedegree to which the measured eye blinking sequence conforms to the modelis determined by processing, through a pattern recognizer, the series ofrespective scores, the pattern recognizer checking the series ofrespective scores for at least one of an abnormal eyelid sequence or anabnormal blink sequence based on the model.

In Example 93, the subject matter of any one of Examples 48 to 92 mayinclude, wherein the pattern recognizer serially checks the abnormalblink sequence after verifying that the eyelid sequence is normal.

In Example 94, the subject matter of any one of Examples 48 to 93 mayinclude, wherein the model defines an eyelid sequence corresponding toeyelids that move in opposite directions as an abnormal eyelid sequence.

Example 97 includes subject matter for facial liveness detection inimage biometrics (such as a device, apparatus, or machine) comprising: abiometric feature detector to detect a set of potential authenticationfaces in an images sequence captured during an authentication attempt bya user; a liveness indication controller to: for each face in the set ofpotential authentication faces: invoke a synchronicity detector to applya set of liveness model tests to the face, each test in the set ofliveness tests including an assay that differs between a live face and aface simulated on a pictorial representation of a face, the assayoperating independently of body arrangement instructions given to theuser; and add the face to a set of authentication faces when the resultof the set of liveness model tests meets a threshold; and anauthentication controller to apply facial authentication to faces in theset of authentication faces.

In Example 98, the subject matter of Example 97 may include, wherein atest in the liveness model of tests includes: an emitter of the systemto emit light towards the face; a sensor of the system to measure aninteraction of the light with the face; and the synchronicity detectorto compare the interaction with a model of a live face.

In Example 99, the subject matter of any one of Examples 97 to 98 mayinclude, wherein the interaction of the light with the face provides adistance measurement between the face and the emitter, wherein the modelof the live face includes an acceptable size range of a live face, andwherein to compare the interaction with the model includes thesynchronicity detector to scale a visual light spectrum representationof the face acquired from the images sequence using the distancemeasurement to determine whether the face is within the acceptable sizerange of a live face.

In Example 100, the subject matter of any one of Examples 97 to 99 mayinclude, wherein the interaction of the light with the face is areflectivity measurement of the character of the light reflected fromthe face.

In Example 101, the subject matter of any one of Examples 97 to 100 mayinclude, wherein to emit the light includes the emitter to emitnon-visual spectrum light, wherein the model of a live face includes anon-uniform positive reflectivity of the light, and wherein to comparethe interaction with the model of the face includes the synchronicitydetector to determine whether the area in which the face was found hasnon-uniform positive reflectivity in the non-visual spectrum light.

In Example 102, the subject matter of any one of Examples 97 to 101 mayinclude, wherein the non-visual spectrum light is infrared light.

In Example 103, the subject matter of any one of Examples 97 to 102 mayinclude, wherein to emit the light include the emitter to emit anon-uniform pattern of visible light, wherein the model of a live facedoes not reflect the pattern, and wherein to compare the interactionwith the model of the face includes the synchronicity detector todetermine whether the pattern is discernable over the face.

In Example 104, the subject matter of any one of Examples 97 to 103 mayinclude, wherein a test in the liveness model of tests includesproviding a target, without an instruction to the user, on a display.

In Example 105, the subject matter of any one of Examples 97 to 104 mayinclude, wherein the target is moving, wherein the test from the set ofliveness model tests includes the biometric feature detector to track aneye gaze of the user with respect to the target, and wherein the assayis an evaluation of how closely the eye gaze tracks the target.

In Example 106, the subject matter of any one of Examples 97 to 105 mayinclude, wherein the target is a frame superimposed on the sequence ofimages re-displayed to the user via a display of the system, wherein thetest from the set of liveness model tests includes the synchronicitydetector to determine a synchronous movement between the user and anenvironmental feature ascertainable from the sequence of images, andwherein the assay is an evaluation of how closely movement of theenvironmental feature is synchronized to movement of the user.

In Example 107, the subject matter of any one of Examples 97 to 106 mayinclude, wherein the target is an icon in a display area an avatar ofthe user, the display area not the sequence of images by anenvironmental feature detector, wherein the test from the set ofliveness model tests includes the synchronicity detector to determine asynchronous movement between the user and an environmental featureascertainable from the sequence of images, and wherein the assay is anevaluation of how closely movement of the environmental feature issynchronized to movement of the user.

In Example 108, the subject matter of any one of Examples 97 to 107 mayinclude, wherein a test in the liveness model of tests includes theenvironmental feature detector identifying an outline conforming to adevice across the sequence of images, and wherein the assay is whetherthe face appears within the outline across a plurality of the sequenceof images.

In Example 109, the subject matter of any one of Examples 97 to 108 mayinclude, wherein whether the face appears within the outline across theplurality of the sequence of images is true if the face appears in anarea of an image in the sequence of images and the outline encloses thearea in a set of preceding images in the sequence of images, the set ofpreceding images selected based on a movement threshold applied to theoutline.

Example 110 includes subject matter for facial liveness detection inimage biometrics (such as a method, means for performing acts, machinereadable medium including instructions that when performed by a machinecause the machine to performs acts, or an apparatus to perform)comprising: detecting a set of potential authentication faces in animages sequence captured during an authentication attempt by a user; foreach face in the set of potential authentication faces, perform theoperations of: applying a set of liveness model tests to the face, eachtest in the set of liveness tests including an assay that differsbetween a live face and a face simulated on a pictorial representationof a face, the assay operating independently of body arrangementinstructions given to the user; and adding the face to a set ofauthentication faces when the result of the set of liveness model testsmeets a threshold; and applying facial authentication to faces in theset of authentication faces.

In Example 111, the subject matter of Example 110 may include, wherein atest in the liveness model of tests includes: emitting light towards theface with an emitter; measuring an interaction of the light with theface; and comparing the interaction with a model of a live face.

In Example 112, the subject matter of any one of Examples 110 to 111 mayinclude, wherein the interaction of the light with the face provides adistance measurement between the face and the emitter, wherein the modelof the live face includes an acceptable size range of a live face, andwherein comparing the interaction with the model includes scaling avisual light spectrum representation of the face acquired from theimages sequence using the distance measurement to determine whether theface is within the acceptable size range of a live face.

In Example 113, the subject matter of any one of Examples 110 to 112 mayinclude, wherein the interaction of the light with the face is areflectivity measurement of the character of the light reflected fromthe face.

In Example 114, the subject matter of any one of Examples 110 to 113 mayinclude, wherein emitting the light includes emitting non-visualspectrum light, wherein the model of a live face includes a non-uniformpositive reflectivity of the light, and wherein comparing theinteraction with the model of the face includes determining whether thearea in which the face was found has non-uniform positive reflectivityin the non-visual spectrum light.

In Example 115, the subject matter of any one of Examples 110 to 114 mayinclude, wherein the non-visual spectrum light is infrared light.

In Example 116, the subject matter of any one of Examples 110 to 115 mayinclude, wherein emitting the light include emitting a non-uniformpattern of visible light, wherein the model of a live face does notreflect the pattern, and wherein comparing the interaction with themodel of the face includes determining whether the pattern isdiscernable over the face.

In Example 117, the subject matter of any one of Examples 110 to 116 mayinclude, wherein a test in the liveness model of tests includesproviding a target, without an instruction to the user, on a display.

In Example 118, the subject matter of any one of Examples 110 to 117 mayinclude, wherein the target is moving, wherein the test from the set ofliveness model tests includes tracking an eye gaze of the user withrespect to the target, and wherein the assay is an evaluation of howclosely the eye gaze tracks the target.

In Example 119, the subject matter of any one of Examples 110 to 118 mayinclude, wherein the target is a frame superimposed on the sequence ofimages re-displayed to the user, wherein the test from the set ofliveness model tests includes determining a synchronous movement betweenthe user and an environmental feature ascertainable from the sequence ofimages, and wherein the assay is an evaluation of how closely movementof the environmental feature is synchronized to movement of the user.

In Example 120, the subject matter of any one of Examples 110 to 119 mayinclude, wherein the target is an icon in a display area an avatar ofthe user, the display area not the sequence of images, wherein the testfrom the set of liveness model tests includes determining a synchronousmovement between the user and an environmental feature ascertainablefrom the sequence of images, and wherein the assay is an evaluation ofhow closely movement of the environmental feature is synchronized tomovement of the user.

In Example 121, the subject matter of any one of Examples 110 to 120 mayinclude, wherein a test in the liveness model of tests includesidentifying an outline conforming to a device across the sequence ofimages, and wherein the assay is whether the face appears within theoutline across a plurality of the sequence of images.

In Example 122, the subject matter of any one of Examples 110 to 121 mayinclude, wherein whether the face appears within the outline across theplurality of the sequence of images is true if the face appears in anarea of an image in the sequence of images and the outline encloses thearea in a set of preceding images in the sequence of images, the set ofpreceding images selected based on a movement threshold applied to theoutline.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments that may bepracticed. These embodiments are also referred to herein as “examples.”Such examples may include elements in addition to those shown ordescribed. However, the present inventors also contemplate examples inwhich only those elements shown or described are provided. Moreover, thepresent inventors also contemplate examples using any combination orpermutation of those elements shown or described (or one or more aspectsthereof), either with respect to a particular example (or one or moreaspects thereof), or with respect to other examples (or one or moreaspects thereof) shown or described herein.

All publications, patents, and patent documents referred to in thisdocument are incorporated by reference herein in their entirety, asthough individually incorporated by reference. In the event ofinconsistent usages between this document and those documents soincorporated by reference, the usage in the incorporated reference(s)should be considered supplementary to that of this document; forirreconcilable inconsistencies, the usage in this document controls.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended, that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim are still deemed to fall within thescope of that claim. Moreover, in the following claims, the terms“first,” “second,” and “third,” etc. are used merely as labels, and arenot intended to impose numerical requirements on their objects.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments may be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is to allow thereader to quickly ascertain the nature of the technical disclosure andis submitted with the understanding that it will not be used tointerpret or limit the scope or meaning of the claims. Also, in theabove Detailed Description, various features may be grouped together tostreamline the disclosure. This should not be interpreted as intendingthat an unclaimed disclosed feature is essential to any claim. Rather,inventive subject matter may lie in less than all features of aparticular disclosed embodiment. Thus, the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate embodiment. The scope of the embodiments should bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A system for facial liveness detection in imagebiometrics, the system comprising: a biometric feature detector todetecting a set of potential authentication faces in an image sequencecaptured during an authentication attempt by a user; a livenessindication controller to: for each face in the set of potentialauthentication faces: use an emitter of the system to emit light towardsthe face; use a sensor of the system to measure an interaction of thelight with the face; and invoke a synchronicity detector to: apply a setof liveness model tests to the face, each test in the set of livenesstests including an assay that differs between a live face and a facesimulated on a pictorial representation of a face, the assay operatingindependently of body arrangement instructions given to the user; andcompare the interaction of the light with the face with a model of alive face, wherein in at least one test in the set of liveness modelstests, the interaction of the light with the face provides a distancemeasurement between the face and the emitter, wherein the model of thelive face includes an acceptable size range of a live face, and whereinto compare the interaction with the model includes the synchronicitydetector to scale a visual light spectrum representation of the faceacquired from the image sequence using the distance measurement todetermine whether the face is within the acceptable size range of a liveface; and add the face to a set of authentication faces when the resultof the set of liveness model tests meets a threshold; and anauthentication controller to apply facial authentication to faces in theset of authentication faces.
 2. The system of claim 1, wherein theinteraction of the light with the face is a reflectivity measurement ofthe character of the light reflected from the face.
 3. The system ofclaim 2, wherein to emit the light includes the emitter to emitnon-visual spectrum light, wherein the model of a live face includes anon-uniform positive reflectivity of the light, and wherein to comparethe interaction with the model of the face includes the synchronicitydetector to determine whether the area in which the face was found hasnon-uniform positive reflectivity in the non-visual spectrum light. 4.The system of claim 2, wherein to emit the light include the emitter toemit a non-uniform pattern of visible light, wherein the model of a liveface does not reflect the pattern, and wherein to compare theinteraction with the model of the face includes the synchronicitydetector to determine whether the pattern is discernable over the face.5. The system of claim 1, wherein another test in the liveness model oftests includes providing a target, without an instruction to the user,on a display.
 6. The system of claim 5, wherein the target is moving,wherein the test from the set of liveness model tests includes thebiometric feature detector to track an eye gaze of the user with respectto the target, and wherein the assay is an evaluation of how closely theeye gaze tracks the target.
 7. The system of claim 6, wherein the targetis a frame superimposed on the sequence of images re-displayed to theuser via a display of the system, wherein the test from the set ofliveness model tests includes the synchronicity detector to determine asynchronous movement between the user and an environmental featureascertainable from the sequence of images, and wherein the assay is anevaluation of how closely movement of the environmental feature issynchronized to movement of the user.
 8. The system of claim 6, whereinthe target is an icon in a display area including an avatar of the user,the display area not including the sequence of images by anenvironmental feature detector, wherein the test from the set ofliveness model tests includes the synchronicity detector to determine asynchronous movement between the user and an environmental featureascertainable from the sequence of images, and wherein the assay is anevaluation of how closely movement of the environmental feature issynchronized to movement of the user.
 9. The system of claim 1, whereinanother test in the liveness model of tests includes the environmentalfeature detector identifying an outline conforming to a device acrossthe sequence of images, and wherein the assay is whether the faceappears within the outline across a plurality of the sequence of images.10. The system of claim 9, wherein whether the face appears within theoutline across the plurality of the sequence of images is true if theface appears in an area of an image in the sequence of images and theoutline encloses the area in a set of preceding images in the sequenceof images, the set of preceding images selected based on a movementthreshold applied to the outline.
 11. A method for facial livenessdetection in image biometrics, the method comprising: detecting a set ofpotential authentication faces in an image sequence captured during anauthentication attempt by a user; for each face in the set of potentialauthentication faces, perform the operations of: emitting light towardsthe face with an emitter; measuring an interaction of the light with theface; comparing the interaction with a model of a live face; andapplying a set of liveness model tests to the face, each test in the setof liveness tests including an assay that differs between a live faceand a face simulated on a pictorial representation of a face, the assayoperating independently of body arrangement instructions given to theuser, wherein in at least one test in the set of liveness models tests,the interaction of the light with the face provides a distancemeasurement between the face and the emitter, wherein the model of thelive face includes an acceptable size range of a live face, and whereinto compare the interaction with the model includes the synchronicitydetector to scale a visual light spectrum representation of the faceacquired from the image sequence using the distance measurement todetermine whether the face is within the acceptable size range of a liveface; and adding the face to a set of authentication faces when theresult of the set of liveness model tests meets a threshold; andapplying facial authentication to faces in the set of authenticationfaces.
 12. The method of claim 11, wherein the interaction of the lightwith the face is a reflectivity measurement of the character of thelight reflected from the face.
 13. The method of claim 12, whereinemitting the light includes emitting non-visual spectrum light, whereinthe model of a live face includes a non-uniform positive reflectivity ofthe light, and wherein comparing the interaction with the model of theface includes determining whether the area in which the face was foundhas non-uniform positive reflectivity in the non-visual spectrum light.14. The method of claim 12, wherein emitting the light include emittinga non-uniform pattern of visible light, wherein the model of a live facedoes not reflect the pattern, and wherein comparing the interaction withthe model of the face includes determining whether the pattern isdiscernable over the face.
 15. The method of claim 11, wherein anothertest in the liveness model of tests includes providing a target, withoutan instruction to the user, on a display.
 16. The method of claim 15,wherein the target is moving, wherein the test from the set of livenessmodel tests includes tracking an eye gaze of the user with respect tothe target, and wherein the assay is an evaluation of how closely theeye gaze tracks the target.
 17. The method of claim 15, wherein thetarget is a frame superimposed on the sequence of images re-displayed tothe user, wherein the test from the set of liveness model tests includesdetermining a synchronous movement between the user and an environmentalfeature ascertainable from the sequence of images, and wherein the assayis an evaluation of how closely movement of the environmental feature issynchronized to movement of the user.
 18. The method of claim 15,wherein the target is an icon in a display area including an avatar ofthe user, the display area not including the sequence of images, whereinthe test from the set of liveness model tests includes determining asynchronous movement between the user and an environmental featureascertainable from the sequence of images, and wherein the assay is anevaluation of how closely movement of the environmental feature issynchronized to movement of the user.
 19. The method of claim 11,wherein another test in the liveness model of tests includes identifyingan outline conforming to a device across the sequence of images, andwherein the assay is whether the face appears within the outline acrossa plurality of the sequence of images.
 20. At least one non-transitorymachine readable medium including instructions for facial livenessdetection in image biometrics, the instructions, when executed by amachine, cause the machine to perform operations comprising: detecting aset of potential authentication faces in an image sequence capturedduring an authentication attempt by a user; for each face in the set ofpotential authentication faces, perform the operations of: emittinglight towards the face with an emitter; measuring an interaction of thelight with the face; comparing the interaction with a model of a liveface; and applying a set of liveness model tests to the face, each testin the set of liveness tests including an assay that differs between alive face and a face simulated on a pictorial representation of a face,the assay operating independently of body arrangement instructions givento the user, wherein in at least one test in the set of liveness modelstests, the interaction of the light with the face provides a distancemeasurement between the face and the emitter, wherein the model of thelive face includes an acceptable size range of a live face, and whereinto compare the interaction with the model includes the synchronicitydetector to scale a visual light spectrum representation of the faceacquired from the image sequence using the distance measurement todetermine whether the face is within the acceptable size range of a liveface; adding the face to a set of authentication faces when the resultof the set of liveness model tests meets a threshold; and applyingfacial authentication to faces in the set of authentication faces. 21.The at least one non-transitory machine readable medium of claim 20,wherein the interaction of the light with the face is a reflectivitymeasurement of the character of the light reflected from the face. 22.The at least one non-transitory machine readable medium of claim 20,wherein another test in the liveness model of tests includes providing atarget, without an instruction to the user, on a display.
 23. The atleast one non-transitory machine readable medium of claim 20, whereinanother test in the liveness model of tests includes identifying anoutline conforming to a device across the sequence of images, andwherein the assay is whether the face appears within the outline acrossa plurality of the sequence of images.